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統計数学セミナー
過去の記録 ～07/26｜次回の予定｜今後の予定 07/27～
| 担当者 | 吉田朋広、増田弘毅、荻原哲平、小池祐太 |
|---|---|
| セミナーURL | http://www.sigmath.es.osaka-u.ac.jp/~kamatani/statseminar/ |
| 目的 | 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う. |
過去の記録
2024年07月23日(火)
15:00-16:10 数理科学研究科棟(駒場) 118号室
ハイブリッド開催
田栗 正隆 氏 (東京医科大学医療データサイエンス分野)
近似的な多重頑健推定量を用いた時間依存性交絡の調整 (日本語)
https://u-tokyo-ac-jp.zoom.us/meeting/register/tZcocOGgrDIpHtIPBLecsHgqaY6tjuNB4Voc
ハイブリッド開催
田栗 正隆 氏 (東京医科大学医療データサイエンス分野)
近似的な多重頑健推定量を用いた時間依存性交絡の調整 (日本語)
[ 講演概要 ]
医学分野の実臨床においては、血圧の値を経時的に評価してその結果次第で降圧薬の投与の有無を決めるといったように、共変量が過去の治療の影響を受けて変化しさらに将来の治療に影響を与えるという状況が生じうる。このような時間依存性交絡が生じる状況では、通常の回帰モデル等による解析では、求めたい治療の因果効果に対してバイアスが生じてしまうことが知られている。この問題に対して、Bang and Robins (2005) は期待値の繰り返しに基づくAIPW（Augmented Inverse Probability Weighting）推定量を提案した。近年、この推定量はデータに仮定する複数のモデル誤特定に対する多重頑健性を持つことが示されている（Díaz et al., 2023）。しかしながら、この手法は本質的にIPWを用いた重み付き推定を行うものであり、重みのバラツキが大きい状況では推定精度が悪くなるという欠点がある。本研究では、IPWの層別化を利用した近似的な多重頑健推定量を提案する。提案手法は、点治療の状況で論じられている傾向スコア層別と回帰モデルを組み合わせる方法の拡張とみなすことができる。提案する手法の性能をシミュレーション実験により評価した結果を報告する。
[ 参考URL ]医学分野の実臨床においては、血圧の値を経時的に評価してその結果次第で降圧薬の投与の有無を決めるといったように、共変量が過去の治療の影響を受けて変化しさらに将来の治療に影響を与えるという状況が生じうる。このような時間依存性交絡が生じる状況では、通常の回帰モデル等による解析では、求めたい治療の因果効果に対してバイアスが生じてしまうことが知られている。この問題に対して、Bang and Robins (2005) は期待値の繰り返しに基づくAIPW（Augmented Inverse Probability Weighting）推定量を提案した。近年、この推定量はデータに仮定する複数のモデル誤特定に対する多重頑健性を持つことが示されている（Díaz et al., 2023）。しかしながら、この手法は本質的にIPWを用いた重み付き推定を行うものであり、重みのバラツキが大きい状況では推定精度が悪くなるという欠点がある。本研究では、IPWの層別化を利用した近似的な多重頑健推定量を提案する。提案手法は、点治療の状況で論じられている傾向スコア層別と回帰モデルを組み合わせる方法の拡張とみなすことができる。提案する手法の性能をシミュレーション実験により評価した結果を報告する。
https://u-tokyo-ac-jp.zoom.us/meeting/register/tZcocOGgrDIpHtIPBLecsHgqaY6tjuNB4Voc
2024年06月28日(金)
13:00-14:10 数理科学研究科棟(駒場) 128号室
ハイブリッド開催
原田 和治 氏 (東京医科大学医療データサイエンス分野)
医学における予測モデルの活用と階層構造を持つ順序回帰の提案 (日本語)
https://u-tokyo-ac-jp.zoom.us/meeting/register/tZUpd-ispjIqG9NfJk7_kjW2pBcvq_KMXHPW
ハイブリッド開催
原田 和治 氏 (東京医科大学医療データサイエンス分野)
医学における予測モデルの活用と階層構造を持つ順序回帰の提案 (日本語)
[ 講演概要 ]
医学における統計学の活用というと，無作為化比較試験の設計や観察研究に基づく因果効果の推定など，ある治療や曝露が心身の状態に及ぼす影響を偏りなく推定する研究課題に関心が向けられることが多い．一方で，尿検査などの侵襲性（医療行為が痛みや出血，健康リスクを伴うこと）の低い検査の結果に基づいて個々人の将来の健康リスクを見積もるといった，予測・分類モデルの構築もまた，関心の高い領域である．本講演では，はじめに医学分野における予測モデルの活用状況について紹介する．次に，非侵襲的に病気を診断したり，状態を推測したりするために用いられる生体分子（バイオマーカー）の探索およびそれらを用いた予測モデル構築を動機として，応答変数が階層構造を持つ順序回帰モデルを導入し，構造的スパース正則化を用いた学習方法を提案する．
[ 参考URL ]医学における統計学の活用というと，無作為化比較試験の設計や観察研究に基づく因果効果の推定など，ある治療や曝露が心身の状態に及ぼす影響を偏りなく推定する研究課題に関心が向けられることが多い．一方で，尿検査などの侵襲性（医療行為が痛みや出血，健康リスクを伴うこと）の低い検査の結果に基づいて個々人の将来の健康リスクを見積もるといった，予測・分類モデルの構築もまた，関心の高い領域である．本講演では，はじめに医学分野における予測モデルの活用状況について紹介する．次に，非侵襲的に病気を診断したり，状態を推測したりするために用いられる生体分子（バイオマーカー）の探索およびそれらを用いた予測モデル構築を動機として，応答変数が階層構造を持つ順序回帰モデルを導入し，構造的スパース正則化を用いた学習方法を提案する．
https://u-tokyo-ac-jp.zoom.us/meeting/register/tZUpd-ispjIqG9NfJk7_kjW2pBcvq_KMXHPW
2024年06月18日(火)
13:00-14:10 数理科学研究科棟(駒場) 128号室
ハイブリッド開催
Lorenzo Mercuri 氏 (University of Milan)
A compound CARMA(p,q)-Hawkes process for pricing financial derivatives (English)
https://u-tokyo-ac-jp.zoom.us/meeting/register/tZ0rcOmvpjwuGNHx8ht0rMs1rD3HcEajoJv6
ハイブリッド開催
Lorenzo Mercuri 氏 (University of Milan)
A compound CARMA(p,q)-Hawkes process for pricing financial derivatives (English)
[ 講演概要 ]
Recently, a new self-exciting point process with a continuous-time autoregressive moving average intensity process, named CARMA(p,q)-Hawkes model, has been introduced. The model generalizes the well-known Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p,q) model where the associated state process is driven by the counting process itself. The new model maintains the same level of tractability of the Hawkes (e.g., Infinitesimal generator, backward and forward Kolmogorov equation, joint characteristic function and so on). However, it is able to reproduce more complex time-dependency structure observed in several market data.
Starting from this model, we introduce a Compound CARMA(p,q)-Hawkes with a random jump size independent of the counting and the intensity processes. This can be used as the main block for a new option pricing model, due to log-affine structure of the characteristic function of the underlying log-price driven by a pure jump compound CARMA(p,q)-Hawkes.
Further, we extend this model by scaling it with a measurable function of the time and the left-limit of the price itself. Exploiting the Markov structure of the new model, we derive the forward Kolmogorov equation that leads us to a Dupire-like formula. Some numerical results will also be presented.
[ 参考URL ]Recently, a new self-exciting point process with a continuous-time autoregressive moving average intensity process, named CARMA(p,q)-Hawkes model, has been introduced. The model generalizes the well-known Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p,q) model where the associated state process is driven by the counting process itself. The new model maintains the same level of tractability of the Hawkes (e.g., Infinitesimal generator, backward and forward Kolmogorov equation, joint characteristic function and so on). However, it is able to reproduce more complex time-dependency structure observed in several market data.
Starting from this model, we introduce a Compound CARMA(p,q)-Hawkes with a random jump size independent of the counting and the intensity processes. This can be used as the main block for a new option pricing model, due to log-affine structure of the characteristic function of the underlying log-price driven by a pure jump compound CARMA(p,q)-Hawkes.
Further, we extend this model by scaling it with a measurable function of the time and the left-limit of the price itself. Exploiting the Markov structure of the new model, we derive the forward Kolmogorov equation that leads us to a Dupire-like formula. Some numerical results will also be presented.
https://u-tokyo-ac-jp.zoom.us/meeting/register/tZ0rcOmvpjwuGNHx8ht0rMs1rD3HcEajoJv6
2024年04月10日(水)
13:30-14:40 数理科学研究科棟(駒場) 126号室
ハイブリッド形式
Ivan Nourdin 氏 (University of Luxembourg)
Limit theorems for additive functionals of stationary Gaussian fields (English)
https://forms.gle/uMKm3gVquLpYaVdc6
ハイブリッド形式
Ivan Nourdin 氏 (University of Luxembourg)
Limit theorems for additive functionals of stationary Gaussian fields (English)
[ 講演概要 ]
In this talk, we will investigate central and non-central limit theorems for additive functionals of stationary Gaussian fields. Our main tool will be the Malliavin-Stein approach. Based on joint works with Nikolai Leonenko, Leonardo Maini and Francesca Pistolato.
[ 参考URL ]In this talk, we will investigate central and non-central limit theorems for additive functionals of stationary Gaussian fields. Our main tool will be the Malliavin-Stein approach. Based on joint works with Nikolai Leonenko, Leonardo Maini and Francesca Pistolato.
https://forms.gle/uMKm3gVquLpYaVdc6
2023年03月08日(水)
14:00- 数理科学研究科棟(駒場) 号室
Zoomによるハイブリッド配信（3/6申込締切）と現地参加（東京大学本郷キャンパス） https://docs.google.com/forms/d/e/1FAIpQLSckefFqzVsTMDOr-5u1JN1_P8gNA7oZduP0QfTSP-OZ-w3qJQ/viewform
Evgeny Spodarev 氏 ( Ulm University, Germany)
Non-ergodic statistics for hamonizable stable processes (English)
https://docs.google.com/forms/d/e/1FAIpQLSet6w12XsqdCGQ8yEe4sOqRlCOhhrJXeKl5H7lMaRy4LZhmqQ/viewform
Zoomによるハイブリッド配信（3/6申込締切）と現地参加（東京大学本郷キャンパス） https://docs.google.com/forms/d/e/1FAIpQLSckefFqzVsTMDOr-5u1JN1_P8gNA7oZduP0QfTSP-OZ-w3qJQ/viewform
Evgeny Spodarev 氏 ( Ulm University, Germany)
Non-ergodic statistics for hamonizable stable processes (English)
[ 講演概要 ]
We consider stationary real harmonizable symmetric α-stable processes X={X(t):t∈ℝ} with a finite control measure. Assuming the control measure is symmetric and absolutely continuous with respect to the Lebesgue measure on the real line, we refer to its density function as the spectral density of X. Standard methods for statistical inference on stable processes cannot be applied as harmonizable stable processes are non-ergodic.
A stationary real harmonizable symmetric α-stable process X admits a LePage series representation and is conditionally Gaussian which allows us to derive the non-ergodic limit of sample functions on X. In particular, we give an explicit expression for the non-ergodic limits of the empirical characteristic function of X and the lag process {X(t+h)−X(t):t∈ℝ} with h>0, respectively.
The process admits an equivalent representation as a series of sinusoidal waves with random frequencies whose probability density function is in fact the (normalized) spectral density of X. Using the strongly consistent frequency estimation via periodograms we present a strongly consistent estimator of the spectral density which is based only on one sampled path of X. The periodogram computation is fast and efficient, and our method is not affected by the non-ergodicity of X. Most notably no prior knowledge on parameters of the process such as its index of stability α is needed.
References:
[1] L.V. Hoang, E. Spodarev, "Inversion of alpha-sine and alpha-cosine transforms on R", Inverse Problems 37 (2021), 085008
[2] L.V. Hoang, E. Spodarev, "Non-ergodic statistics and spectral density estimation for stationary real harmonizable symmetric α-stable processes", Preprint arXiv:2209.04315, submitted, 2022.
[ 参考URL ]We consider stationary real harmonizable symmetric α-stable processes X={X(t):t∈ℝ} with a finite control measure. Assuming the control measure is symmetric and absolutely continuous with respect to the Lebesgue measure on the real line, we refer to its density function as the spectral density of X. Standard methods for statistical inference on stable processes cannot be applied as harmonizable stable processes are non-ergodic.
A stationary real harmonizable symmetric α-stable process X admits a LePage series representation and is conditionally Gaussian which allows us to derive the non-ergodic limit of sample functions on X. In particular, we give an explicit expression for the non-ergodic limits of the empirical characteristic function of X and the lag process {X(t+h)−X(t):t∈ℝ} with h>0, respectively.
The process admits an equivalent representation as a series of sinusoidal waves with random frequencies whose probability density function is in fact the (normalized) spectral density of X. Using the strongly consistent frequency estimation via periodograms we present a strongly consistent estimator of the spectral density which is based only on one sampled path of X. The periodogram computation is fast and efficient, and our method is not affected by the non-ergodicity of X. Most notably no prior knowledge on parameters of the process such as its index of stability α is needed.
References:
[1] L.V. Hoang, E. Spodarev, "Inversion of alpha-sine and alpha-cosine transforms on R", Inverse Problems 37 (2021), 085008
[2] L.V. Hoang, E. Spodarev, "Non-ergodic statistics and spectral density estimation for stationary real harmonizable symmetric α-stable processes", Preprint arXiv:2209.04315, submitted, 2022.
https://docs.google.com/forms/d/e/1FAIpQLSet6w12XsqdCGQ8yEe4sOqRlCOhhrJXeKl5H7lMaRy4LZhmqQ/viewform
2023年01月10日(火)
10:50-11:30 数理科学研究科棟(駒場) 号室
現地参加（駒場Iキャンパス16号館827号室）とZoomによるハイブリッド配信
井口優雅 氏 (University College London)
Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions
(現地参加) https://forms.gle/qwssLccVgsAWcfps7 (Zoom参加) (1/8迄) https://docs.google.com/forms/d/e/1FAIpQLSe7OYeMDfaZ7pTLO42k43Tn5dWKpsyw
現地参加（駒場Iキャンパス16号館827号室）とZoomによるハイブリッド配信
井口優雅 氏 (University College London)
Parameter Estimation with Increased Precision for Elliptic and Hypo-elliptic Diffusions
[ 講演概要 ]
Parametric inference for multi-dimensional diffusion processes has been studied over the past decades. Established approaches for likelihood-based estimation invoke a numerical time-discretisation scheme for the approximation of the (typically intractable) transition dynamics of the Stochastic Differential Equation (SDE) over finite time periods. Especially in the setting of some class of hypo-elliptic models, recent research (Ditlevsen and Samson 2019, Gloter and Yoshida 2021) has highlighted the critical effect of the choice of numerical scheme on the behaviour of derived parameter estimates. In our work, first, we develop two weak second order ‘sampling schemes' (to cover both the hypo-elliptic and elliptic classes) and generate accompanying ‘transition density schemes' of the SDE (i.e., approximations of the SDE transition density). Then, we produce a collection of analytic results, providing a complete theoretical framework that solidifies the proposed schemes and showcases advantages from their incorporation within SDE calibration methods, in both high and low frequency observations regime. We also present numerical results from carrying out classical or Bayesian inference. This is a joint work with Alexandros Beskos and Matthew Graham, and the preprint is available at https://arxiv.org/abs/2211.16384.
[ 参考URL ]Parametric inference for multi-dimensional diffusion processes has been studied over the past decades. Established approaches for likelihood-based estimation invoke a numerical time-discretisation scheme for the approximation of the (typically intractable) transition dynamics of the Stochastic Differential Equation (SDE) over finite time periods. Especially in the setting of some class of hypo-elliptic models, recent research (Ditlevsen and Samson 2019, Gloter and Yoshida 2021) has highlighted the critical effect of the choice of numerical scheme on the behaviour of derived parameter estimates. In our work, first, we develop two weak second order ‘sampling schemes' (to cover both the hypo-elliptic and elliptic classes) and generate accompanying ‘transition density schemes' of the SDE (i.e., approximations of the SDE transition density). Then, we produce a collection of analytic results, providing a complete theoretical framework that solidifies the proposed schemes and showcases advantages from their incorporation within SDE calibration methods, in both high and low frequency observations regime. We also present numerical results from carrying out classical or Bayesian inference. This is a joint work with Alexandros Beskos and Matthew Graham, and the preprint is available at https://arxiv.org/abs/2211.16384.
(現地参加) https://forms.gle/qwssLccVgsAWcfps7 (Zoom参加) (1/8迄) https://docs.google.com/forms/d/e/1FAIpQLSe7OYeMDfaZ7pTLO42k43Tn5dWKpsyw
2022年12月05日(月)
14:40-15:50 数理科学研究科棟(駒場) 号室
現地参加（統計数理研究所）とZoomによるハイブリッド配信 （※状況によりオンライン配信のみとなる可能性もございます）
Michael Choi 氏 (National University of Singapore and Yale-NUS College)
A binary branching model with Moran-type interactions (English)
(Zoom参加) 12/1締切https://docs.google.com/forms/d/e/1FAIpQLSdyluSozvNOGmDcXzGv496v2AQNiPePqIerLaBN9pD4wxEmnw/viewform (現地参加) 先着20名https://forms.gle/rS9rjhL2jXo6eGgt5
現地参加（統計数理研究所）とZoomによるハイブリッド配信 （※状況によりオンライン配信のみとなる可能性もございます）
Michael Choi 氏 (National University of Singapore and Yale-NUS College)
A binary branching model with Moran-type interactions (English)
[ 講演概要 ]
Branching processes naturally arise as pertinent models in a variety of applications such as population size dynamics, neutron transport and cell proliferation kinetics. A key result for understanding the behaviour of such systems is the Perron Frobenius decomposition, which allows one to characterise the large time average behaviour of the branching process via its leading eigenvalue and corresponding left and right eigenfunctions. However, obtaining estimates of these quantities can be challenging, for example when the branching process is spatially dependent with inhomogeneous rates. In this talk, I will introduce a new interacting particle model that combines the natural branching behaviour of the underlying process with a selection and resampling mechanism, which allows one to maintain some control over the system and more efficiently estimate the eigenelements. I will then present the main result, which provides an explicit relation between the particle system and the branching process via a many-to-one formula and also quantifies the L^2 distance between the occupation measures of the two systems. Finally, I will discuss some examples in order to illustrate the scope and possible extensions of the model, and to provide some comparisons with the Fleming Viot interacting particle system. This is based on work with Alex Cox (University of Bath) and Denis Villemonais (Université de Lorraine).
[ 参考URL ]Branching processes naturally arise as pertinent models in a variety of applications such as population size dynamics, neutron transport and cell proliferation kinetics. A key result for understanding the behaviour of such systems is the Perron Frobenius decomposition, which allows one to characterise the large time average behaviour of the branching process via its leading eigenvalue and corresponding left and right eigenfunctions. However, obtaining estimates of these quantities can be challenging, for example when the branching process is spatially dependent with inhomogeneous rates. In this talk, I will introduce a new interacting particle model that combines the natural branching behaviour of the underlying process with a selection and resampling mechanism, which allows one to maintain some control over the system and more efficiently estimate the eigenelements. I will then present the main result, which provides an explicit relation between the particle system and the branching process via a many-to-one formula and also quantifies the L^2 distance between the occupation measures of the two systems. Finally, I will discuss some examples in order to illustrate the scope and possible extensions of the model, and to provide some comparisons with the Fleming Viot interacting particle system. This is based on work with Alex Cox (University of Bath) and Denis Villemonais (Université de Lorraine).
(Zoom参加) 12/1締切https://docs.google.com/forms/d/e/1FAIpQLSdyluSozvNOGmDcXzGv496v2AQNiPePqIerLaBN9pD4wxEmnw/viewform (現地参加) 先着20名https://forms.gle/rS9rjhL2jXo6eGgt5
2022年10月21日(金)
①14:30-15:40- ②16:20-17:30 数理科学研究科棟(駒場) 126号室
ハイブリッド開催
Estate Khmaladze 氏 (Victoria University of Wellington)
On the theory of distribution free testing of statistical hypothesis
①Empirical processes for discrete and continuous observations: structure, difficulties and resolution.
②Further testing problems: parametric regression and Markov chains. (ENGLISH)
https://docs.google.com/forms/d/e/1FAIpQLScxh_wNRs3WbMUG4S3cGlGAu1ZkP4trLbc08CBrvUDO66hwNg/viewform?usp=sf_link
ハイブリッド開催
Estate Khmaladze 氏 (Victoria University of Wellington)
On the theory of distribution free testing of statistical hypothesis
①Empirical processes for discrete and continuous observations: structure, difficulties and resolution.
②Further testing problems: parametric regression and Markov chains. (ENGLISH)
[ 講演概要 ]
The concept of distribution free testing is familiar to all. Everybody, who heard about rank statistics, knows that the distribution of ranks is independent from the distribution of underlying random variables, provided this later is a continuous distribution on the real line. Everybody, who ever used classical goodness of fit tests like Kolmogorov - Smirnov test or Cram\'er-von Mises test, knows that the distribution of statistics of these tests is independent from the distribution of the underlying random variables, again, provided this distribution is a continuous distribution on the real line.
Development in subsequent decades revealed many cracks in existing theory and difficulties in extending the concept of distribution free testing to majority of interesting models. It gradually became clear that the new starting point is needed to expand the theory to these models.
In our lectures we first describe the current situation in empirical and related processes. Then we describe how the new approaches have been developed and what progress has been made.
Then we hope to show how the new approach can be naturally extended to the domain of stochastic processes, and how the important probabilistic models of the processes can be tested in distribution free way. In discrete time, results for Markov chains have been published in 2021. Extension to continuous time will be explored during the current visit to University of Tokyo.
[ 参考URL ]The concept of distribution free testing is familiar to all. Everybody, who heard about rank statistics, knows that the distribution of ranks is independent from the distribution of underlying random variables, provided this later is a continuous distribution on the real line. Everybody, who ever used classical goodness of fit tests like Kolmogorov - Smirnov test or Cram\'er-von Mises test, knows that the distribution of statistics of these tests is independent from the distribution of the underlying random variables, again, provided this distribution is a continuous distribution on the real line.
Development in subsequent decades revealed many cracks in existing theory and difficulties in extending the concept of distribution free testing to majority of interesting models. It gradually became clear that the new starting point is needed to expand the theory to these models.
In our lectures we first describe the current situation in empirical and related processes. Then we describe how the new approaches have been developed and what progress has been made.
Then we hope to show how the new approach can be naturally extended to the domain of stochastic processes, and how the important probabilistic models of the processes can be tested in distribution free way. In discrete time, results for Markov chains have been published in 2021. Extension to continuous time will be explored during the current visit to University of Tokyo.
https://docs.google.com/forms/d/e/1FAIpQLScxh_wNRs3WbMUG4S3cGlGAu1ZkP4trLbc08CBrvUDO66hwNg/viewform?usp=sf_link
2022年10月19日(水)
10:30-11:40 数理科学研究科棟(駒場) 号室
完全オンライン形式で開催
山岸 颯 氏 (東京大学大学院数理科学研究科)
fractional Brownian motion(fBm)に関係する汎関数のオーダー評価とfBmで駆動される確率微分方程式の二次変分の漸近展開について
https://docs.google.com/forms/d/e/1FAIpQLSd3i_gFci4Dc8T8gjtMigm08aIoQH6gM_Yfw0bHfppM1CNmag/viewform?usp=sf_link
完全オンライン形式で開催
山岸 颯 氏 (東京大学大学院数理科学研究科)
fractional Brownian motion(fBm)に関係する汎関数のオーダー評価とfBmで駆動される確率微分方程式の二次変分の漸近展開について
[ 講演概要 ]
混合正規分布に収束するSkorohod積分の漸近展開の理論に基づき，fBmで駆動される確率微分方程式の二次変分で表される汎関数の漸近展開公式を得た．
汎関数の確率展開や漸近展開公式に現れるランダムな表象を求めることが一般論の適用において必要となるが，その際，ランダムなウェイトを持つfBmの反復積分の積の和で表される汎関数が複数現れ，これらのオーダーを繰り返し評価することが求められる． 今回講演者はこのオーダー評価を機械的に行うために和の構造を捉えた重み付きグラフを用いた指数を導入した．この講演はarXiv:2206.00323に基づく．
[ 参考URL ]混合正規分布に収束するSkorohod積分の漸近展開の理論に基づき，fBmで駆動される確率微分方程式の二次変分で表される汎関数の漸近展開公式を得た．
汎関数の確率展開や漸近展開公式に現れるランダムな表象を求めることが一般論の適用において必要となるが，その際，ランダムなウェイトを持つfBmの反復積分の積の和で表される汎関数が複数現れ，これらのオーダーを繰り返し評価することが求められる． 今回講演者はこのオーダー評価を機械的に行うために和の構造を捉えた重み付きグラフを用いた指数を導入した．この講演はarXiv:2206.00323に基づく．
https://docs.google.com/forms/d/e/1FAIpQLSd3i_gFci4Dc8T8gjtMigm08aIoQH6gM_Yfw0bHfppM1CNmag/viewform?usp=sf_link
2022年07月21日(木)
13:30-14:40 数理科学研究科棟(駒場) -号室
オンライン開催
川野 秀一 氏 (電気通信大学大学院情報理工学研究科)
クロネッカー積表現に基づくテンソルデータに対する共通成分分析
https://forms.gle/JrtVRcQNgn9pug3F7
オンライン開催
川野 秀一 氏 (電気通信大学大学院情報理工学研究科)
クロネッカー積表現に基づくテンソルデータに対する共通成分分析
[ 講演概要 ]
次元削減を行うためのデータ解析手法の一つとして，共通成分分析と呼ばれる手法がある．共通成分分析は，共分散構造の観点から多母集団間の共通した特徴を抽出することにより，データの潜在的な線形構造を探索する手法である．本報告では，テンソル構造を持つデータに対し，共通成分分析の拡張を試みる．クロネッカー積に基づく方法でモデルを定式化し，推定アルゴリズムを導出する．導出したアルゴリズムと初期値の設定に関する理論的な結果も紹介する．なお，本研究は，NTTデータ数理システムの吉川剛平氏との共同研究である．
[ 参考URL ]次元削減を行うためのデータ解析手法の一つとして，共通成分分析と呼ばれる手法がある．共通成分分析は，共分散構造の観点から多母集団間の共通した特徴を抽出することにより，データの潜在的な線形構造を探索する手法である．本報告では，テンソル構造を持つデータに対し，共通成分分析の拡張を試みる．クロネッカー積に基づく方法でモデルを定式化し，推定アルゴリズムを導出する．導出したアルゴリズムと初期値の設定に関する理論的な結果も紹介する．なお，本研究は，NTTデータ数理システムの吉川剛平氏との共同研究である．
https://forms.gle/JrtVRcQNgn9pug3F7
2022年02月16日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomでの開催となります。参加希望の方はGoogle Formより前日までにご登録ください。
Teppei Ogihara 氏 (University of Tokyo)
Efficient estimation for ergodic jump-diffusion processes
https://docs.google.com/forms/d/e/1FAIpQLSeRTEo19DJgFiVsEpLrRapqzkL6LZAiUMGdA0ezK-nWYSPrGg/viewform
Zoomでの開催となります。参加希望の方はGoogle Formより前日までにご登録ください。
Teppei Ogihara 氏 (University of Tokyo)
Efficient estimation for ergodic jump-diffusion processes
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
We study the estimation problem of the parametric model for ergodic jump-diffusion processes. Shimizu and Yoshida (Stat. Inference Stoch. Process. 2006) proposed a quasi-maximum-likelihood estimator based on a thresholding likelihood function that detects the existence of jumps.
In this talk, we consider the efficiency of estimators by using local asymptotic normality (LAN). To show the LAN property, we need to specify the asymptotic behavior of log-likelihood ratios, which is complicated for the jump-diffusion model because the transition probability for no jump is quite different from that for the presence of jumps. We develop techniques to show the LAN property based on transition density approximation. By applying these techniques to the thresholding likelihood function, we obtain the LAN property for the jump-diffusion model. Moreover, we have the asymptotic efficiency of
the quasi-maximum-likelihood estimator in Shimizu and Yoshida (2006) and a Bayes-type estimator proposed in Ogihara and Yoshida (Stat.Inference Stoch. Process. 2011). This is a joint work with Yuma Uehara (Kansai University).
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
We study the estimation problem of the parametric model for ergodic jump-diffusion processes. Shimizu and Yoshida (Stat. Inference Stoch. Process. 2006) proposed a quasi-maximum-likelihood estimator based on a thresholding likelihood function that detects the existence of jumps.
In this talk, we consider the efficiency of estimators by using local asymptotic normality (LAN). To show the LAN property, we need to specify the asymptotic behavior of log-likelihood ratios, which is complicated for the jump-diffusion model because the transition probability for no jump is quite different from that for the presence of jumps. We develop techniques to show the LAN property based on transition density approximation. By applying these techniques to the thresholding likelihood function, we obtain the LAN property for the jump-diffusion model. Moreover, we have the asymptotic efficiency of
the quasi-maximum-likelihood estimator in Shimizu and Yoshida (2006) and a Bayes-type estimator proposed in Ogihara and Yoshida (Stat.Inference Stoch. Process. 2011). This is a joint work with Yuma Uehara (Kansai University).
https://docs.google.com/forms/d/e/1FAIpQLSeRTEo19DJgFiVsEpLrRapqzkL6LZAiUMGdA0ezK-nWYSPrGg/viewform
2022年01月20日(木)
15:00-16:10 数理科学研究科棟(駒場) 号室
植松良公 氏 (東北大学大学院経済学研究科)
On weak factor models
https://docs.google.com/forms/d/e/1FAIpQLSdH8oP72k7-qsHigZBBZ4F6N-bGIJ6BcOWgKLhted2ohGSBeg/viewform
植松良公 氏 (東北大学大学院経済学研究科)
On weak factor models
[ 講演概要 ]
本講演では、従来の近似的ファクターモデルよりもシグナルの弱い「weak factor model」について、最近のわれわれの研究成果を報告する。はじめに、因子負荷行列のスパース性によって誘導される「sparsity-induced weak factor model」を定義し、その推定方法と推定量の収束レートを導出する。さらに、因子負荷行列のスパース性を検証するため、偽発見率をコントロールした多重検定の方法を紹介する。
[ 参考URL ]本講演では、従来の近似的ファクターモデルよりもシグナルの弱い「weak factor model」について、最近のわれわれの研究成果を報告する。はじめに、因子負荷行列のスパース性によって誘導される「sparsity-induced weak factor model」を定義し、その推定方法と推定量の収束レートを導出する。さらに、因子負荷行列のスパース性を検証するため、偽発見率をコントロールした多重検定の方法を紹介する。
https://docs.google.com/forms/d/e/1FAIpQLSdH8oP72k7-qsHigZBBZ4F6N-bGIJ6BcOWgKLhted2ohGSBeg/viewform
2022年01月19日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomでの開催となります。参加希望の方はGoogle Formより前日までにご登録ください。
Martin Hazelton 氏 (Otago University)
Dynamic fibre samplers for linear inverse problems
https://docs.google.com/forms/d/e/1FAIpQLSeoXt5v8xdQNFAKTDLoD0lttaHjV17_r7864x11mtxU1EQlhQ/viewform
Zoomでの開催となります。参加希望の方はGoogle Formより前日までにご登録ください。
Martin Hazelton 氏 (Otago University)
Dynamic fibre samplers for linear inverse problems
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Statistical inverse problems occur when we wish to learn about some random process that is observed only indirectly. Inference in such situations typically involves sampling possible values for the latent variables of interest conditional on the indirect observations. For count data, the latent variables are constrained to lie on a fibre (solution set for the linear system) comprising the integer lattice within a convex polytope.
Sampling the latent counts can be conducted using MCMC methods,through a random walk on this fibre. A major challenge is finding a set of basic moves that ensures connectedness of the walk over the fibre. In principle this can be done by computing a Markov basis of potential moves, but the resulting sampler can be hugely inefficient even when such a basis is computable. In this talk I will describe some current work on developing a dynamic Markov basis that generates moves on the fly. This approach can guarantee irreducibility of the sampler while gaining efficiency by increasing the probability of selecting serviceable sampling directions.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Statistical inverse problems occur when we wish to learn about some random process that is observed only indirectly. Inference in such situations typically involves sampling possible values for the latent variables of interest conditional on the indirect observations. For count data, the latent variables are constrained to lie on a fibre (solution set for the linear system) comprising the integer lattice within a convex polytope.
Sampling the latent counts can be conducted using MCMC methods,through a random walk on this fibre. A major challenge is finding a set of basic moves that ensures connectedness of the walk over the fibre. In principle this can be done by computing a Markov basis of potential moves, but the resulting sampler can be hugely inefficient even when such a basis is computable. In this talk I will describe some current work on developing a dynamic Markov basis that generates moves on the fly. This approach can guarantee irreducibility of the sampler while gaining efficiency by increasing the probability of selecting serviceable sampling directions.
https://docs.google.com/forms/d/e/1FAIpQLSeoXt5v8xdQNFAKTDLoD0lttaHjV17_r7864x11mtxU1EQlhQ/viewform
2021年12月15日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Estate Khmaladze 氏 (Victoria University of Wellington)
Theory of Distribution-free Testing
https://docs.google.com/forms/d/e/1FAIpQLSdFj1XF8WJSPRmE0GFKY2QxscaGxC9msM6GkEsAf0TgD9yv2g/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Estate Khmaladze 氏 (Victoria University of Wellington)
Theory of Distribution-free Testing
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
The aim of the talk is to introduce transformations of empirical-type processes by a group of unitary operators. Recall that if v_{nP} is empirical process on real line, based on a sample from P, it can be mapped into empirical process v_{nQ} by appropriate change of time
v_{nP}(h(x))=v_{nQ}(x)
where h(x) is continuous and increasing. This is the basis for distribution-free theory of goodness of fit testing. If w(\phi) is a function-parametric “empirical-type” process (i.e. has functions \phi from a space L as a time) and if K* is a unitary operator on L, then transformed process Kw we define as
Kw(\phi) = w(K*\phi)
These two formulas have good similarity, but one transformation in on the real line, while the other transformation in on functional space.This later one turns out to be of very broad use, and allows to base distribution-free theory upon it. Examples, we have specific results for, are parametric empirical
processes in R^d, regression empirical processes, those in GLM, parametric models for point processes and for Markov processes in discrete time. Hopefully, further examples will follow.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
The aim of the talk is to introduce transformations of empirical-type processes by a group of unitary operators. Recall that if v_{nP} is empirical process on real line, based on a sample from P, it can be mapped into empirical process v_{nQ} by appropriate change of time
v_{nP}(h(x))=v_{nQ}(x)
where h(x) is continuous and increasing. This is the basis for distribution-free theory of goodness of fit testing. If w(\phi) is a function-parametric “empirical-type” process (i.e. has functions \phi from a space L as a time) and if K* is a unitary operator on L, then transformed process Kw we define as
Kw(\phi) = w(K*\phi)
These two formulas have good similarity, but one transformation in on the real line, while the other transformation in on functional space.This later one turns out to be of very broad use, and allows to base distribution-free theory upon it. Examples, we have specific results for, are parametric empirical
processes in R^d, regression empirical processes, those in GLM, parametric models for point processes and for Markov processes in discrete time. Hopefully, further examples will follow.
https://docs.google.com/forms/d/e/1FAIpQLSdFj1XF8WJSPRmE0GFKY2QxscaGxC9msM6GkEsAf0TgD9yv2g/viewform
2021年11月17日(水)
15:30-17:00 数理科学研究科棟(駒場) 号室
Zoomでの開催となります。参加希望の方はGoogle Formより２日前までにご登録ください。
Jean Bertoin 氏 (Institut of Mathematics, University of Zurich (UZH))
On the local times of noise reinforced Bessel processes
https://docs.google.com/forms/d/e/1FAIpQLSeuK9AOw6QUqvUge9ukw__v04j5jpfogzrGxlPLpEgNhW09kg/viewform
Zoomでの開催となります。参加希望の方はGoogle Formより２日前までにご登録ください。
Jean Bertoin 氏 (Institut of Mathematics, University of Zurich (UZH))
On the local times of noise reinforced Bessel processes
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Bessel processes form a one-parameter family of self-similar diffusion on $[0,\infty)$ with the same Hurst exponent 1/2 as Brownian motion. Loosely speaking, in this setting, linear noise reinforcement with reinforcement parameter $p$ consists of repeating (if $p>0$) or counterbalancing (if $p<0$)infinitesimal increments of the process, uniformly at random and at a fixed rate as time passes. In this talk, we will investigate the effect of noise reinforcement on the local time at level $0$, that is, informally, the time that the process spends at $0$. A connection with increasing self-similar Markov processes will play a key role.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Bessel processes form a one-parameter family of self-similar diffusion on $[0,\infty)$ with the same Hurst exponent 1/2 as Brownian motion. Loosely speaking, in this setting, linear noise reinforcement with reinforcement parameter $p$ consists of repeating (if $p>0$) or counterbalancing (if $p<0$)infinitesimal increments of the process, uniformly at random and at a fixed rate as time passes. In this talk, we will investigate the effect of noise reinforcement on the local time at level $0$, that is, informally, the time that the process spends at $0$. A connection with increasing self-similar Markov processes will play a key role.
https://docs.google.com/forms/d/e/1FAIpQLSeuK9AOw6QUqvUge9ukw__v04j5jpfogzrGxlPLpEgNhW09kg/viewform
2021年10月13日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Li Cheng 氏 (National University of Singapore (NUS))
Bayesian Fixed-domain Asymptotics for Covariance Parameters in Gaussian Random Field Models
https://docs.google.com/forms/d/e/1FAIpQLSfEWrpkVavWEELx93dPxd0g2thhkC8NtA_8We4cDeiCKI6mZg/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Li Cheng 氏 (National University of Singapore (NUS))
Bayesian Fixed-domain Asymptotics for Covariance Parameters in Gaussian Random Field Models
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Gaussian random field models are commonly used for modeling spatial processes. In this work we focus on the Gaussian process with isotropic Matern covariance functions. Under fixed-domain asymptotics,it is well known that when the dimension of data is less than or equal to three, the microergodic parameter can be consistently estimated with asymptotic normality while the range (or length-scale) parameter cannot. Motivated by this frequentist result, we prove that under a Bayesian fixed-domain framework, the posterior distribution of the microergodic parameter converges in total variation norm to a normal distribution with shrinking variance, while the posterior of the range parameter does not necessarily converge. Built on this new theory, we further show that the Bayesian kriging predictor satisfies the posterior asymptotic efficiency in linear prediction. We illustrate these asymptotic results in numerical examples.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Gaussian random field models are commonly used for modeling spatial processes. In this work we focus on the Gaussian process with isotropic Matern covariance functions. Under fixed-domain asymptotics,it is well known that when the dimension of data is less than or equal to three, the microergodic parameter can be consistently estimated with asymptotic normality while the range (or length-scale) parameter cannot. Motivated by this frequentist result, we prove that under a Bayesian fixed-domain framework, the posterior distribution of the microergodic parameter converges in total variation norm to a normal distribution with shrinking variance, while the posterior of the range parameter does not necessarily converge. Built on this new theory, we further show that the Bayesian kriging predictor satisfies the posterior asymptotic efficiency in linear prediction. We illustrate these asymptotic results in numerical examples.
https://docs.google.com/forms/d/e/1FAIpQLSfEWrpkVavWEELx93dPxd0g2thhkC8NtA_8We4cDeiCKI6mZg/viewform
2021年09月15日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Anup Biswas 氏 (Indian Institute of Science Education and Research (IISER), Pune)
Ergodic risk-sensitive control: history, new results and open problems
https://docs.google.com/forms/d/e/1FAIpQLSe-136jVBQwRDg3rgEGpgVtH2d4chXCvQuvnk_gE2fZqMGwBw/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Anup Biswas 氏 (Indian Institute of Science Education and Research (IISER), Pune)
Ergodic risk-sensitive control: history, new results and open problems
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Risk-sensitive control became popular because of the robustness it provides to the optimal control. Its connection to the theory of large deviation also made it a natural candidate of mathematical interest. In this talk, we shall give an overview of the history of risk-sensitive control problems and some of its applications. We shall then (informally) discuss the ways of tackling this problem and the main questions of interest. At the end, we shall see some important open problems.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Risk-sensitive control became popular because of the robustness it provides to the optimal control. Its connection to the theory of large deviation also made it a natural candidate of mathematical interest. In this talk, we shall give an overview of the history of risk-sensitive control problems and some of its applications. We shall then (informally) discuss the ways of tackling this problem and the main questions of interest. At the end, we shall see some important open problems.
https://docs.google.com/forms/d/e/1FAIpQLSe-136jVBQwRDg3rgEGpgVtH2d4chXCvQuvnk_gE2fZqMGwBw/viewform
2021年08月18日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Gery Geenens 氏 (The University of New South Wales (UNSW Sydney))
Dependence, Sklar's copulas and discreteness
https://docs.google.com/forms/d/e/1FAIpQLScU9_QHdHZ-JeVyUIJOKUFmYJvG697NBDFkNh735WK9Cov1Og/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Gery Geenens 氏 (The University of New South Wales (UNSW Sydney))
Dependence, Sklar's copulas and discreteness
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Copulas have now become ubiquitous statistical tools for describing, analysing and modelling dependence between random variables. Yet the classical copula approach, building on Sklar’s theorem, cannot be legitimised if the variables of interest are not continuous. Indeed in the presence of discreteness, copula models are (i) unidentifiable, and (ii) not margin-free, and this by construction. In spite of the serious inconsistencies that this creates, downplaying statements are widespread in the literature, where copula methods are devised and used in discrete settings. In this work we call to reconsidering this current practice. To reconcile copulas with discreteness, we argued that they should be apprehended from a more fundamental perspective. Inspired by century-old ideas of Yule, we propose a novel construction which allows all the pleasant properties of copulas for modelling dependence (in particular:‘margin-freeness’) to smoothly carry over to the discrete setting.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Copulas have now become ubiquitous statistical tools for describing, analysing and modelling dependence between random variables. Yet the classical copula approach, building on Sklar’s theorem, cannot be legitimised if the variables of interest are not continuous. Indeed in the presence of discreteness, copula models are (i) unidentifiable, and (ii) not margin-free, and this by construction. In spite of the serious inconsistencies that this creates, downplaying statements are widespread in the literature, where copula methods are devised and used in discrete settings. In this work we call to reconsidering this current practice. To reconcile copulas with discreteness, we argued that they should be apprehended from a more fundamental perspective. Inspired by century-old ideas of Yule, we propose a novel construction which allows all the pleasant properties of copulas for modelling dependence (in particular:‘margin-freeness’) to smoothly carry over to the discrete setting.
https://docs.google.com/forms/d/e/1FAIpQLScU9_QHdHZ-JeVyUIJOKUFmYJvG697NBDFkNh735WK9Cov1Og/viewform
2021年07月14日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Anirvan Chakraborty 氏 ( IISER Kolkata, India)
Statistics for Functional Data
https://docs.google.com/forms/d/e/1FAIpQLSfkHbmXT_3kHkBIUedzNSFqQ6QxuZzUQ9_qOgc8HqtZsKHTPQ/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Anirvan Chakraborty 氏 ( IISER Kolkata, India)
Statistics for Functional Data
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
With the advancement in technology, statisticians often have to analyze data which are curves or functions observed over a domain. Data of this type is usually called functional data and is very common these days in various fields of science. Statistical modelling of this type of data is usually done by viewing the data as a random sample from a probability distribution on some infinite dimensional function space. This formulation, however, implies that one has to delve into the mathematical rigour and complexity of dealing with infinite dimensional objects and probability distributions in function spaces. As such, standard multivariate statistical methods are far from useful in analyzing such data. We will discuss some statistical techniques for analyzing functional data as well as outline some of the unique challenges faced and also discuss some interesting open problems in this frontline research area.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
With the advancement in technology, statisticians often have to analyze data which are curves or functions observed over a domain. Data of this type is usually called functional data and is very common these days in various fields of science. Statistical modelling of this type of data is usually done by viewing the data as a random sample from a probability distribution on some infinite dimensional function space. This formulation, however, implies that one has to delve into the mathematical rigour and complexity of dealing with infinite dimensional objects and probability distributions in function spaces. As such, standard multivariate statistical methods are far from useful in analyzing such data. We will discuss some statistical techniques for analyzing functional data as well as outline some of the unique challenges faced and also discuss some interesting open problems in this frontline research area.
https://docs.google.com/forms/d/e/1FAIpQLSfkHbmXT_3kHkBIUedzNSFqQ6QxuZzUQ9_qOgc8HqtZsKHTPQ/viewform
2021年07月03日(土)
10:55-17:10 数理科学研究科棟(駒場) 号室
確率過程の統計解析のためのRパッケージYUIMAをもちいた「確率微分方程式のデータサイエンス入門」をZoomでおこないます．
- 氏 (-)
-
http://www.sigmath.es.osaka-u.ac.jp/statmodel/?page_id=2028
確率過程の統計解析のためのRパッケージYUIMAをもちいた「確率微分方程式のデータサイエンス入門」をZoomでおこないます．
- 氏 (-)
-
[ 講演概要 ]
確率微分方程式のデータサイエンス入門 2021
7月3日（土）
10:55 – 12:00 YUIMAパッケージの基本（Zoomサーバ不具合のため時間変更）
13:00 – 14:10 qmle, 漸近正規性，信頼区間，統計推測
14:30 – 15:40 qmle, 漸近正規性，信頼区間，統計推測
16:00 – 17:10 高頻度データ解析入門
7月4日（日）
13:00 – 14:10 adaBayesとベイズ統計学への応用
14:30 – 15:40 レヴィ過程の基本と応用
16:00 – 17:10 レヴィ過程の基本と応用
17:20 – フリーディスカッション
YUIMAパッケージを通じて，確率微分方程式の直感的理解とシミュレーション，およびモデリングについてのスキルを習得できます．PCを用いた実習も行います．大学初年次程度の微分積分の知識が必要です．また，R言語の知識があるとよりスムーズです． 幅広い分野の学生・研究者・社会人の参加を歓迎します．
・ご参加いただくためにはZoomのアプリケーションをインストールしていただく必要があります．なお．アカウントを取得する必要はございません．
・各講座はある程度独立に行うことを予定しているため，１講座のみからでもご参加いただけます．
・実習のためR言語を実行できる環境でご参加ください．チュートリアル開始までにR言語をインストールしてください．また，下記の要領で最新のyuimaパッケージのインストールをお願いします．
・参加無料
[ 参考URL ]確率微分方程式のデータサイエンス入門 2021
7月3日（土）
10:55 – 12:00 YUIMAパッケージの基本（Zoomサーバ不具合のため時間変更）
13:00 – 14:10 qmle, 漸近正規性，信頼区間，統計推測
14:30 – 15:40 qmle, 漸近正規性，信頼区間，統計推測
16:00 – 17:10 高頻度データ解析入門
7月4日（日）
13:00 – 14:10 adaBayesとベイズ統計学への応用
14:30 – 15:40 レヴィ過程の基本と応用
16:00 – 17:10 レヴィ過程の基本と応用
17:20 – フリーディスカッション
YUIMAパッケージを通じて，確率微分方程式の直感的理解とシミュレーション，およびモデリングについてのスキルを習得できます．PCを用いた実習も行います．大学初年次程度の微分積分の知識が必要です．また，R言語の知識があるとよりスムーズです． 幅広い分野の学生・研究者・社会人の参加を歓迎します．
・ご参加いただくためにはZoomのアプリケーションをインストールしていただく必要があります．なお．アカウントを取得する必要はございません．
・各講座はある程度独立に行うことを予定しているため，１講座のみからでもご参加いただけます．
・実習のためR言語を実行できる環境でご参加ください．チュートリアル開始までにR言語をインストールしてください．また，下記の要領で最新のyuimaパッケージのインストールをお願いします．
・参加無料
http://www.sigmath.es.osaka-u.ac.jp/statmodel/?page_id=2028
2021年06月16日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Hiroki Masuda 氏 (Kyushu University)
Levy-Ornstein-Uhlenbeck Regression
https://docs.google.com/forms/d/e/1FAIpQLSfkHbmXT_3kHkBIUedzNSFqQ6QxuZzUQ9_qOgc8HqtZsKHTPQ/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより２日前までにご登録ください。
Hiroki Masuda 氏 (Kyushu University)
Levy-Ornstein-Uhlenbeck Regression
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
We will present some of recent developments in parametric inference for a linear regression model driven by a non-Gaussian stable Levy process, when the process is observed at high frequency over a fixed time period. The model depends on a covariate process and the finite-dimensional parameter: the stability index (activity index) and the scale in the noise term, and the (auto)regression coefficients in the trend term, all being unknown. The maximum-likelihood estimator is shown to be asymptotically mixed-normally distributed with maximum concentration property. In order to bypass possible multiple-root problem and heavy numerical optimization, we also consider some easily computable initial estimator with which the one-step improvement does work. The asymptotic properties hold true in a unified manner regardless of whether the model is stationary and/or ergodic, almost without taking care of character of the
covariate process. Also discussed will be model-selection issues and some possible model extensions.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
We will present some of recent developments in parametric inference for a linear regression model driven by a non-Gaussian stable Levy process, when the process is observed at high frequency over a fixed time period. The model depends on a covariate process and the finite-dimensional parameter: the stability index (activity index) and the scale in the noise term, and the (auto)regression coefficients in the trend term, all being unknown. The maximum-likelihood estimator is shown to be asymptotically mixed-normally distributed with maximum concentration property. In order to bypass possible multiple-root problem and heavy numerical optimization, we also consider some easily computable initial estimator with which the one-step improvement does work. The asymptotic properties hold true in a unified manner regardless of whether the model is stationary and/or ergodic, almost without taking care of character of the
covariate process. Also discussed will be model-selection issues and some possible model extensions.
https://docs.google.com/forms/d/e/1FAIpQLSfkHbmXT_3kHkBIUedzNSFqQ6QxuZzUQ9_qOgc8HqtZsKHTPQ/viewform
2021年05月19日(水)
14:30-16:00 オンライン開催
参加希望の方は以下のGoogle Formより３日前までにご登録ください。 ご登録後、会議参加に必要なURLを送付いたします。
Federico Camia 氏 (NYU Abu Dhabi)
Limit Theorems and Random Fractal Curves in Statistical Mechanics (ENGLISH)
https://docs.google.com/forms/d/e/1FAIpQLSe2ObhY3dsFUUU4EaRyslLiAwfuA6chMmiw5uyfa1bvKMdyfg/viewform
参加希望の方は以下のGoogle Formより３日前までにご登録ください。 ご登録後、会議参加に必要なURLを送付いたします。
Federico Camia 氏 (NYU Abu Dhabi)
Limit Theorems and Random Fractal Curves in Statistical Mechanics (ENGLISH)
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Statistical mechanics deals with systems that have a large number of components whose behavior can often be considered random. For this reason, probability theory plays an essential role in the mathematical analysis of the subject. After a gentle introduction to statistical mechanics, I will give a nontechnical overview of recent, exciting developments that combine in a beautiful and unexpected way discrete probability, stochastic processes and complex analysis. For concreteness, I will discuss two specific models, percolation and the Ising model, which have a long history, have played an important part in the development of statistical mechanics, and occupy a central place in modern probability theory.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Statistical mechanics deals with systems that have a large number of components whose behavior can often be considered random. For this reason, probability theory plays an essential role in the mathematical analysis of the subject. After a gentle introduction to statistical mechanics, I will give a nontechnical overview of recent, exciting developments that combine in a beautiful and unexpected way discrete probability, stochastic processes and complex analysis. For concreteness, I will discuss two specific models, percolation and the Ising model, which have a long history, have played an important part in the development of statistical mechanics, and occupy a central place in modern probability theory.
https://docs.google.com/forms/d/e/1FAIpQLSe2ObhY3dsFUUU4EaRyslLiAwfuA6chMmiw5uyfa1bvKMdyfg/viewform
2021年05月19日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより３日前までにご登録ください。
Federico Camia 氏 (NYU Abu Dhabi)
Limit Theorems and Random Fractal Curves in Statistical Mechanics
https://docs.google.com/forms/d/e/1FAIpQLSe2ObhY3dsFUUU4EaRyslLiAwfuA6chMmiw5uyfa1bvKMdyfg/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより３日前までにご登録ください。
Federico Camia 氏 (NYU Abu Dhabi)
Limit Theorems and Random Fractal Curves in Statistical Mechanics
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Statistical mechanics deals with systems that have a large number of components whose behavior can often be considered random. For this reason, probability theory plays an essential role in the mathematical analysis of the subject. After a gentle introduction to statistical mechanics, I will give a nontechnical overview of recent, exciting developments that combine in a beautiful and unexpected way discrete probability, stochastic processes and complex analysis. For concreteness, I will discuss two specific models, percolation and the Ising model, which have a long history, have played an important part in the development of statistical mechanics, and occupy a central place in modern probability theory.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Statistical mechanics deals with systems that have a large number of components whose behavior can often be considered random. For this reason, probability theory plays an essential role in the mathematical analysis of the subject. After a gentle introduction to statistical mechanics, I will give a nontechnical overview of recent, exciting developments that combine in a beautiful and unexpected way discrete probability, stochastic processes and complex analysis. For concreteness, I will discuss two specific models, percolation and the Ising model, which have a long history, have played an important part in the development of statistical mechanics, and occupy a central place in modern probability theory.
https://docs.google.com/forms/d/e/1FAIpQLSe2ObhY3dsFUUU4EaRyslLiAwfuA6chMmiw5uyfa1bvKMdyfg/viewform
2021年04月21日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomでの開催となります。３日前までに講演参考URLから参加申込みをしてください。
Han Liang Gan 氏 (University of Waikato)
Stationary distribution approximations for two-island and seed bank models (ENGLISH)
https://docs.google.com/forms/d/e/1FAIpQLSfLezQNquom7pjodIrc1suI0o5rsWg9AHNv7cix0A7h39tx-g/viewform
Zoomでの開催となります。３日前までに講演参考URLから参加申込みをしてください。
Han Liang Gan 氏 (University of Waikato)
Stationary distribution approximations for two-island and seed bank models (ENGLISH)
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Two-island Wright-Fisher models are used to model genetic frequencies and variability for subdivided populations. One of the key components of the model is the level of migration between the two
islands. We show that as the population size increases, the appropriate approximation and limit for the stationary distribution of a two-island Wright-Fisher Markov chain depends on the level of migration. In a seed bank model, individuals in one of the islands stay dormant rather than reproduce. We give analogous results for the seed bank model, compare and contrast the differences and examine the effect the seed bank has on genetic variability. Our results are derived from a new development of Stein's method for the two-island diffusion model and existing results for Stein's method for the Dirichlet distribution.
This talk is based on joint work with Adrian Röllin, Nathan Ross and Maite Wilke Berenguer.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Two-island Wright-Fisher models are used to model genetic frequencies and variability for subdivided populations. One of the key components of the model is the level of migration between the two
islands. We show that as the population size increases, the appropriate approximation and limit for the stationary distribution of a two-island Wright-Fisher Markov chain depends on the level of migration. In a seed bank model, individuals in one of the islands stay dormant rather than reproduce. We give analogous results for the seed bank model, compare and contrast the differences and examine the effect the seed bank has on genetic variability. Our results are derived from a new development of Stein's method for the two-island diffusion model and existing results for Stein's method for the Dirichlet distribution.
This talk is based on joint work with Adrian Röllin, Nathan Ross and Maite Wilke Berenguer.
https://docs.google.com/forms/d/e/1FAIpQLSfLezQNquom7pjodIrc1suI0o5rsWg9AHNv7cix0A7h39tx-g/viewform
2021年04月21日(水)
14:30-16:00 数理科学研究科棟(駒場) 号室
Zoomで配信します。 参加希望の方は以下のGoogle Formより３日前までにご登録ください。
Han Liang Gan 氏 (University of Waikato)
Stationary distribution approximations for two-island and seed bank models
https://docs.google.com/forms/d/e/1FAIpQLSfLezQNquom7pjodIrc1suI0o5rsWg9AHNv7cix0A7h39tx-g/viewform
Zoomで配信します。 参加希望の方は以下のGoogle Formより３日前までにご登録ください。
Han Liang Gan 氏 (University of Waikato)
Stationary distribution approximations for two-island and seed bank models
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Two-island Wright-Fisher models are used to model genetic frequencies and variability for subdivided populations. One of the key components of the model is the level of migration between the two islands. We show that as the population size increases, the appropriate approximation and limit for the stationary distribution of a two-island Wright-Fisher Markov chain depends on the level of migration. In a seed bank model, individuals in one of the islands stay dormant rather than reproduce. We give analogous results for the seed bank model, compare and contrast the differences and examine the effect the seed bank has on genetic variability. Our results are derived from a new development of Stein's method for the two-island diffusion model and existing results for
Stein's method for the Dirichlet distribution.
This talk is based on joint work with Adrian Röllin, Nathan Ross and Maite Wilke Berenguer.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Two-island Wright-Fisher models are used to model genetic frequencies and variability for subdivided populations. One of the key components of the model is the level of migration between the two islands. We show that as the population size increases, the appropriate approximation and limit for the stationary distribution of a two-island Wright-Fisher Markov chain depends on the level of migration. In a seed bank model, individuals in one of the islands stay dormant rather than reproduce. We give analogous results for the seed bank model, compare and contrast the differences and examine the effect the seed bank has on genetic variability. Our results are derived from a new development of Stein's method for the two-island diffusion model and existing results for
Stein's method for the Dirichlet distribution.
This talk is based on joint work with Adrian Röllin, Nathan Ross and Maite Wilke Berenguer.
https://docs.google.com/forms/d/e/1FAIpQLSfLezQNquom7pjodIrc1suI0o5rsWg9AHNv7cix0A7h39tx-g/viewform
