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統計数学セミナー
過去の記録 ～04/25｜次回の予定｜今後の予定 04/26～
| 担当者 | 吉田朋広、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
2010年03月29日(月)
13:00-14:10 数理科学研究科棟(駒場) 002号室
Catherine Laredo 氏 (MIA, INRA)
Inference for partially observed Markov processes and applications
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/17.html
Catherine Laredo 氏 (MIA, INRA)
Inference for partially observed Markov processes and applications
[ 講演概要 ]
We present some statistical methods for estimating the param- eters of a population dynamics model of annual plants. It is modelled using multitype branching processes with immigration. The data consist of counts in each type that are measured in several populations for a few consecu- tive years. Parametric inference is first carried out when count data of all types are observed. We prove statistical identifiability for all the parameters ruling the population dynamics model and derive consistent and asymptot- ically Gaussian estimators. However, it often occurs that, in practice, one or more types cannot be observed, leading to partially observed processes. Parametric inference is first studied in the case of Poisson distributions. We characterize the parameter subset where identifiability holds and de- rive consistent and asymptotically normal estimators for this parameter subset. Theses results are then extended to other distributions.
We apply these results to feral oilseed data. The model takes account of reproduction, immigration, and seed survival in a seed bank. The data consist of the number of plants in several developmental stages that were measured in a number of populations for few consecutive years. They are incomplete since seeds could not be counted.
[ 参考URL ]We present some statistical methods for estimating the param- eters of a population dynamics model of annual plants. It is modelled using multitype branching processes with immigration. The data consist of counts in each type that are measured in several populations for a few consecu- tive years. Parametric inference is first carried out when count data of all types are observed. We prove statistical identifiability for all the parameters ruling the population dynamics model and derive consistent and asymptot- ically Gaussian estimators. However, it often occurs that, in practice, one or more types cannot be observed, leading to partially observed processes. Parametric inference is first studied in the case of Poisson distributions. We characterize the parameter subset where identifiability holds and de- rive consistent and asymptotically normal estimators for this parameter subset. Theses results are then extended to other distributions.
We apply these results to feral oilseed data. The model takes account of reproduction, immigration, and seed survival in a seed bank. The data consist of the number of plants in several developmental stages that were measured in a number of populations for few consecutive years. They are incomplete since seeds could not be counted.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/17.html
2010年03月15日(月)
15:00-16:00 数理科学研究科棟(駒場) 002号室
Cecilia Mancini 氏 (University of Florence)
BROWNIAN COVARIATION AND CO-JUMPS, GIVEN DISCRETE OBSERVATION
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/16.html
Cecilia Mancini 氏 (University of Florence)
BROWNIAN COVARIATION AND CO-JUMPS, GIVEN DISCRETE OBSERVATION
[ 講演概要 ]
We consider two processes driven by Brownian motions plus drift and possibly infinite activity jumps.
Given discrete observations we separately estimate the covariation between the two Brownian parts and the sum of the co-jumps. This allows to gain insight into the dependence structure of the processes and has important applications in finance.
Our estimator is based on a threshold principle allowing to isolate the jumps over a threshold.
The estimator of the continuous covariation is asymptotically Gaussian and converges at speed square root of n when the jump components have finite variation. In presence infinite variation jumps the speed is heavily influenced both by the small jumps dependence structure and by their jump activity indexes.
This talk is based on Mancini and Gobbi (2009), and Mancini (2010).
[ 参考URL ]We consider two processes driven by Brownian motions plus drift and possibly infinite activity jumps.
Given discrete observations we separately estimate the covariation between the two Brownian parts and the sum of the co-jumps. This allows to gain insight into the dependence structure of the processes and has important applications in finance.
Our estimator is based on a threshold principle allowing to isolate the jumps over a threshold.
The estimator of the continuous covariation is asymptotically Gaussian and converges at speed square root of n when the jump components have finite variation. In presence infinite variation jumps the speed is heavily influenced both by the small jumps dependence structure and by their jump activity indexes.
This talk is based on Mancini and Gobbi (2009), and Mancini (2010).
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/16.html
2010年03月15日(月)
14:00-15:00 数理科学研究科棟(駒場) 002号室
Alexandre Brouste 氏 (Université du Maine)
Statistical inference in the partial observation setting, in continuous time
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/15.html
Alexandre Brouste 氏 (Université du Maine)
Statistical inference in the partial observation setting, in continuous time
[ 講演概要 ]
In various fields, the {\\it signal} process, whose law depends on an unknown parameter $ artheta \\in \\Theta \\subset \\R^p$, can not be observed directly but only through an {\\it observation} process. We will talk about the so called fractional partial observation setting, where the observation process $Y=\\left( Y_t, t \\geq 0 ight)$ is given by a stochastic differential equation: egin{equation} \\label{mod:modelgeneral} Y_t = Y_0 + \\int_0^t h(X_s, artheta) ds + \\sigma W^H_t\\,, \\quad t > 0 \\end{equation} where the function $ h: \\, \\R imes \\Theta \\longrightarrow \\R$ and the constant $\\sigma>0$ are known and the noise $\\left( W^H_t\\,, t\\geq 0 ight)$ is a fractional Brownian motion valued in $\\R$ independent of the signal process $X$ and the initial condition $Y_0$. In this setting, the estimation of the unknown parameter $ artheta \\in \\Theta$ given the observation of the continuous sample path $Y^T=\\left( Y_t , 0 \\leq t \\leq T ight)$, $T>0$, naturally arises.
[ 参考URL ]In various fields, the {\\it signal} process, whose law depends on an unknown parameter $ artheta \\in \\Theta \\subset \\R^p$, can not be observed directly but only through an {\\it observation} process. We will talk about the so called fractional partial observation setting, where the observation process $Y=\\left( Y_t, t \\geq 0 ight)$ is given by a stochastic differential equation: egin{equation} \\label{mod:modelgeneral} Y_t = Y_0 + \\int_0^t h(X_s, artheta) ds + \\sigma W^H_t\\,, \\quad t > 0 \\end{equation} where the function $ h: \\, \\R imes \\Theta \\longrightarrow \\R$ and the constant $\\sigma>0$ are known and the noise $\\left( W^H_t\\,, t\\geq 0 ight)$ is a fractional Brownian motion valued in $\\R$ independent of the signal process $X$ and the initial condition $Y_0$. In this setting, the estimation of the unknown parameter $ artheta \\in \\Theta$ given the observation of the continuous sample path $Y^T=\\left( Y_t , 0 \\leq t \\leq T ight)$, $T>0$, naturally arises.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/15.html
2010年02月17日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
清 智也 氏 (東京大学 情報理工学系研究科)
勾配写像で表される球面上の確率分布族
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/14.html
清 智也 氏 (東京大学 情報理工学系研究科)
勾配写像で表される球面上の確率分布族
[ 講演概要 ]
球面上の確率分布族は、方向統計学において重要である。本講演では、コスト凸関数 (c-凸関数)と呼ばれる関数とその勾配写像を用いて、球面上の分布族を構成する。 コスト凸関数とは、最適輸送理論の分野で導入された概念であり、ユークリッド空間 における凸関数をリーマン多様体の場合へ拡張させたものである。提案する分布族の 性質をいくつか示し、簡単な方向データの解析例を示す。
[ 参考URL ]球面上の確率分布族は、方向統計学において重要である。本講演では、コスト凸関数 (c-凸関数)と呼ばれる関数とその勾配写像を用いて、球面上の分布族を構成する。 コスト凸関数とは、最適輸送理論の分野で導入された概念であり、ユークリッド空間 における凸関数をリーマン多様体の場合へ拡張させたものである。提案する分布族の 性質をいくつか示し、簡単な方向データの解析例を示す。
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/14.html
2009年12月21日(月)
15:00-16:10 数理科学研究科棟(駒場) 128号室
Thomas Simon 氏 (Universite de Lille 1)
Absolute continuity of Ornstein-Uhlenbeck processes
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/13.html
Thomas Simon 氏 (Universite de Lille 1)
Absolute continuity of Ornstein-Uhlenbeck processes
[ 講演概要 ]
Let X be a multidimensional Ornstein-Uhlenbeck process, solution to the S.D.E.
dX = AX + dB
where A is a real nxn matrix and B a Lévy process. We show that when A is non-singular, the law of X_1 is absolutely continuous if and only if the jumping measure of B fulfils a certain geometric condition with respect to A and the Gaussian part of B, which we call the exhaustion property. This optimal criterion is much weaker than for B, which might be very singular and genuinely one-dimensional. The proof uses a certain time derivation procedure and basic arguments from controllability theory.
[ 参考URL ]Let X be a multidimensional Ornstein-Uhlenbeck process, solution to the S.D.E.
dX = AX + dB
where A is a real nxn matrix and B a Lévy process. We show that when A is non-singular, the law of X_1 is absolutely continuous if and only if the jumping measure of B fulfils a certain geometric condition with respect to A and the Gaussian part of B, which we call the exhaustion property. This optimal criterion is much weaker than for B, which might be very singular and genuinely one-dimensional. The proof uses a certain time derivation procedure and basic arguments from controllability theory.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/13.html
2009年12月16日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
Stefano Maria Iacus 氏 (Department of Economics, Business and Statistics, University of Milan, Italy)
ecent results on volatility change point analysis for discretely sampled stochastic differential equations
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/12.html
Stefano Maria Iacus 氏 (Department of Economics, Business and Statistics, University of Milan, Italy)
ecent results on volatility change point analysis for discretely sampled stochastic differential equations
[ 講演概要 ]
In this seminar we review recent advances on change point analysis for the volatility component of stochastic differential equations under different discrete sampling schemes. We consider both ergodic and high frequency and non ergodic cases. Results have been obtained by means of least squares, CUSUM tests and quasi-maximum likelihood approach. We show an application to the recent financial crisis and finally present a Monte Carlo study to compare the three methods under different setups.
Join work with Prof. Nakahiro Yoshida.
[ 参考URL ]In this seminar we review recent advances on change point analysis for the volatility component of stochastic differential equations under different discrete sampling schemes. We consider both ergodic and high frequency and non ergodic cases. Results have been obtained by means of least squares, CUSUM tests and quasi-maximum likelihood approach. We show an application to the recent financial crisis and finally present a Monte Carlo study to compare the three methods under different setups.
Join work with Prof. Nakahiro Yoshida.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/12.html
2009年12月14日(月)
14:00-15:10 数理科学研究科棟(駒場) 128号室
L. VOSTRIKOVA 氏 (LAREMA, Departement de Mathematiques, Universite d’Angers, FRANCE)
On the stability of contingent claimes in incomplet models under statistical estimations.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/11.html
L. VOSTRIKOVA 氏 (LAREMA, Departement de Mathematiques, Universite d’Angers, FRANCE)
On the stability of contingent claimes in incomplet models under statistical estimations.
[ 講演概要 ]
In exponential semi-martingale setting for risky asset we estimate the difference of prices of options when initial physical measure P and corresponding martingale measure Q change to tilde{P} and tilde{Q} respectively. Then, we estimate L1 distance of option’s prices for corresponding parametric models with known and estimated parameters. The results are applied to exponential Levy models with special choise of martingale measure as Esscher measure, minimal entropy measure and f^q -minimal martingale measure. We illustrate our results by considering GMY and CGMY models.
[ 参考URL ]In exponential semi-martingale setting for risky asset we estimate the difference of prices of options when initial physical measure P and corresponding martingale measure Q change to tilde{P} and tilde{Q} respectively. Then, we estimate L1 distance of option’s prices for corresponding parametric models with known and estimated parameters. The results are applied to exponential Levy models with special choise of martingale measure as Esscher measure, minimal entropy measure and f^q -minimal martingale measure. We illustrate our results by considering GMY and CGMY models.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/11.html
2009年12月09日(水)
15:00-16:10 数理科学研究科棟(駒場) 002号室
佐藤 整尚 氏 (統計数理研究所)
分離情報最尤法を使った高頻度金融データにおける実現分散、共分散の推定について
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/10.html
佐藤 整尚 氏 (統計数理研究所)
分離情報最尤法を使った高頻度金融データにおける実現分散、共分散の推定について
[ 講演概要 ]
近年、金融データを使った分析の中で、高頻度データを用いるものが多くなってきている。 しかしながら、通常のヒストリカルな推定法で求めた分散、共分散ではバイアスが発生することが知られており、その一致推定量を求めることがこの分野で盛んに研究されてきている。 本報告では新たに開発された分離情報最尤法(SIML)を用いた推定法を紹介するとともにその性質に関して議論していきたい。さらに、非常に広範囲な応用可能性についても紹介する。
[ 参考URL ]近年、金融データを使った分析の中で、高頻度データを用いるものが多くなってきている。 しかしながら、通常のヒストリカルな推定法で求めた分散、共分散ではバイアスが発生することが知られており、その一致推定量を求めることがこの分野で盛んに研究されてきている。 本報告では新たに開発された分離情報最尤法(SIML)を用いた推定法を紹介するとともにその性質に関して議論していきたい。さらに、非常に広範囲な応用可能性についても紹介する。
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/10.html
2009年11月27日(金)
13:40-14:50 数理科学研究科棟(駒場) 128号室
加藤 賢悟 氏 (広島大学大学院理学研究科数学専攻)
非線形時系列モデルのイノベーション密度の推定
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/09.html
加藤 賢悟 氏 (広島大学大学院理学研究科数学専攻)
非線形時系列モデルのイノベーション密度の推定
[ 講演概要 ]
In this talk, we consider the problem of estimating the innovation density in nonlinear autoregressive models. Specifically, we establish the convergence rate of the supremum distance between the residual-based kernel density estimator and the kernel density estimator using the unobservable actual innovation variables. The proof of the main theorem relies on empirical process theory instead of the conventional Taylor expansion approach. As applications, we obtain the exact rate of weak uniform consistency on the whole line, pointwise asymptotic normality of the residual-based kernel density estimator and the asymptotic distribution of a Bickel-Rosenblatt type global measure statistic related to it. We also examine the conditions of the main theorem for some specic time series model.
[ 参考URL ]In this talk, we consider the problem of estimating the innovation density in nonlinear autoregressive models. Specifically, we establish the convergence rate of the supremum distance between the residual-based kernel density estimator and the kernel density estimator using the unobservable actual innovation variables. The proof of the main theorem relies on empirical process theory instead of the conventional Taylor expansion approach. As applications, we obtain the exact rate of weak uniform consistency on the whole line, pointwise asymptotic normality of the residual-based kernel density estimator and the asymptotic distribution of a Bickel-Rosenblatt type global measure statistic related to it. We also examine the conditions of the main theorem for some specic time series model.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/09.html
2009年10月23日(金)
15:00-16:10 数理科学研究科棟(駒場) 128号室
Vladimir Bogachev 氏 (Moscow State University)
On invariant measures of diffusion processes with unbounded drifts
[ 参考URL ]
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/08.html
Vladimir Bogachev 氏 (Moscow State University)
On invariant measures of diffusion processes with unbounded drifts
[ 参考URL ]
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/08.html
2009年10月22日(木)
16:30-17:40 数理科学研究科棟(駒場) 122号室
深澤 正彰 氏 (大阪大学 金融・保険教育研究センター)
ASYMPTOTICALLY EFFICIENT DISCRETE HEDGING
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/07.html
深澤 正彰 氏 (大阪大学 金融・保険教育研究センター)
ASYMPTOTICALLY EFFICIENT DISCRETE HEDGING
[ 講演概要 ]
The notion of asymptotic efficiency for discrete hedging is introduced and a discretizing strategy which is asymptotically efficient is given explicitly. A lower bound for asymptotic risk of discrete hedging is given, which is attained by a simple discretization scheme. Numerical results for delta hedging in the Black-Scholes model are also presented.
[ 参考URL ]The notion of asymptotic efficiency for discrete hedging is introduced and a discretizing strategy which is asymptotically efficient is given explicitly. A lower bound for asymptotic risk of discrete hedging is given, which is attained by a simple discretization scheme. Numerical results for delta hedging in the Black-Scholes model are also presented.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/07.html
2009年10月21日(水)
15:00-16:10 数理科学研究科棟(駒場) 002号室
田中 冬彦 氏 (科学技術振興機構さきがけ)
AR過程の優調和事前分布と偏自己相関係数による表示
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/06.html
田中 冬彦 氏 (科学技術振興機構さきがけ)
AR過程の優調和事前分布と偏自己相関係数による表示
[ 講演概要 ]
Tanaka and Komaki(2008)では時系列データが2次の自己回帰過程(AR過程)に従う 時のスペクトル密度の推定を考え、優調和事前分布に基づいたベイズスペクトル 密度の方がジェフリーズ事前分布に基づいたベイズスペクトル密度よりも精度よ く推定できることを示している。高次のAR過程での優調和事前分布はTanaka( 2009)によって初めて与えられたが、特性方程式の根を用いた表示のため、数値 実験を行う上では取り扱いづらかった。本発表では高次のAR過程への応用を念頭 において偏自己相関係数(PAC)によるパラメータ表示を導入し数値実験した結 果を紹介する。 また、PACパラメータによる表示は解析的な取扱いをする上でも利点があり、AR 過程の優調和事前分布に関して新しく得られた結果も幾つか紹介したい。
[ 参考URL ]Tanaka and Komaki(2008)では時系列データが2次の自己回帰過程(AR過程)に従う 時のスペクトル密度の推定を考え、優調和事前分布に基づいたベイズスペクトル 密度の方がジェフリーズ事前分布に基づいたベイズスペクトル密度よりも精度よ く推定できることを示している。高次のAR過程での優調和事前分布はTanaka( 2009)によって初めて与えられたが、特性方程式の根を用いた表示のため、数値 実験を行う上では取り扱いづらかった。本発表では高次のAR過程への応用を念頭 において偏自己相関係数(PAC)によるパラメータ表示を導入し数値実験した結 果を紹介する。 また、PACパラメータによる表示は解析的な取扱いをする上でも利点があり、AR 過程の優調和事前分布に関して新しく得られた結果も幾つか紹介したい。
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/06.html
2009年09月30日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
矢田 和善 氏 (筑波大学大学院数理物質科学研究科)
HDLSSデータにおけるPCAについて
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/05.html
矢田 和善 氏 (筑波大学大学院数理物質科学研究科)
HDLSSデータにおけるPCAについて
[ 講演概要 ]
マイクロアレイデータなどに見られるように,データの次元数dが標本数nよりも遥かに大きな高次元小標本(HDLSS)データが,解析対象になる場面が増えてきている.
HDLSSデータに対して従来の統計手法を用いると,次元の呪いによって解析が上手くいかない.解決策の一つとして次元縮約法があり, その一つにPCAがある.高次元における従来型のPCAの漸近的性質は,正規性もしくは同等な仮定のもとで,先行研究が多数存在する. しかしながら,これら仮定は,HDLSSを研究する上で,厳しい制約にもなっている. Yata and Aoshimaの一連の研究は,この制約条件の枠を外すことから始まった.HDLSSにおける従来型PCAの限界は何か?推測が一致性をもつための標本数nと 次元数dの関係が,オーダー条件として明らかにされる.従来型PCAの限界を超える手法は何か?一つの実用的な方法として,クロス行列と呼ばれるデータの変換行列が導入され, この行列の特異値分解に基づいた新しいPCAが提案される.
当日は,マイクロアレイデータによる実例と,シミュレーション結果も交えながら,お話します.本研究は,筑波大学数理物質科学研究科の青嶋誠先生との共同研究です.
[ 参考URL ]マイクロアレイデータなどに見られるように,データの次元数dが標本数nよりも遥かに大きな高次元小標本(HDLSS)データが,解析対象になる場面が増えてきている.
HDLSSデータに対して従来の統計手法を用いると,次元の呪いによって解析が上手くいかない.解決策の一つとして次元縮約法があり, その一つにPCAがある.高次元における従来型のPCAの漸近的性質は,正規性もしくは同等な仮定のもとで,先行研究が多数存在する. しかしながら,これら仮定は,HDLSSを研究する上で,厳しい制約にもなっている. Yata and Aoshimaの一連の研究は,この制約条件の枠を外すことから始まった.HDLSSにおける従来型PCAの限界は何か?推測が一致性をもつための標本数nと 次元数dの関係が,オーダー条件として明らかにされる.従来型PCAの限界を超える手法は何か?一つの実用的な方法として,クロス行列と呼ばれるデータの変換行列が導入され, この行列の特異値分解に基づいた新しいPCAが提案される.
当日は,マイクロアレイデータによる実例と,シミュレーション結果も交えながら,お話します.本研究は,筑波大学数理物質科学研究科の青嶋誠先生との共同研究です.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/05.html
2009年06月03日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
山田 亮 氏 (東京大学医科学研究所 ヒトゲノム解析センター ゲノム機能解析分野)
遺伝的多様性を捉える
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/04.html
山田 亮 氏 (東京大学医科学研究所 ヒトゲノム解析センター ゲノム機能解析分野)
遺伝的多様性を捉える
[ 講演概要 ]
遺伝統計学は、遺伝子の多様性と生物個体の特徴(形質)の多様性との間の関係を検出するための方法を提供する学問分野である。
遺伝情報はDNAの塩基配列にその多くが刻まれているが、昨今、このDNA配列に関する実験技法が急速に発展し、同一種内のDNA配列が、非常に多様かつ不均一な集団を構成していることが明らかになってきた。
DNA配列多様性と形質多様性との関係を検出するにあたり、このDNA配列集団の多様性と不均一性は、遺伝因子間の非独立性として、関係検出過程に大きな影響を与えることから、DNA配列集団の多様性の把握そのものが、遺伝統計学の課題となっている。
ヒトDNA配列は4種類の塩基が長さ30億であるため、『生命体として成立しうる』という制約の下、非常に多様な配列を取り得る。このDNA配列が取り得る範囲をDNA配列の空間とみなしたとき、DNA配列集団の多様性は、その空間におけるDNA配列集団の分布状態となる。
本セミナーでは、DNA配列集団の分布状態を捉える方法について検討する。
[ 参考URL ]遺伝統計学は、遺伝子の多様性と生物個体の特徴(形質)の多様性との間の関係を検出するための方法を提供する学問分野である。
遺伝情報はDNAの塩基配列にその多くが刻まれているが、昨今、このDNA配列に関する実験技法が急速に発展し、同一種内のDNA配列が、非常に多様かつ不均一な集団を構成していることが明らかになってきた。
DNA配列多様性と形質多様性との関係を検出するにあたり、このDNA配列集団の多様性と不均一性は、遺伝因子間の非独立性として、関係検出過程に大きな影響を与えることから、DNA配列集団の多様性の把握そのものが、遺伝統計学の課題となっている。
ヒトDNA配列は4種類の塩基が長さ30億であるため、『生命体として成立しうる』という制約の下、非常に多様な配列を取り得る。このDNA配列が取り得る範囲をDNA配列の空間とみなしたとき、DNA配列集団の多様性は、その空間におけるDNA配列集団の分布状態となる。
本セミナーでは、DNA配列集団の分布状態を捉える方法について検討する。
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/04.html
2009年05月12日(火)
16:20-17:30 数理科学研究科棟(駒場) 126号室
塩濱 敬之 氏 (東京理科大学, 工学部)
Asymptitically efficient estimation of multiple change points in GARCH types models
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/03.html
塩濱 敬之 氏 (東京理科大学, 工学部)
Asymptitically efficient estimation of multiple change points in GARCH types models
[ 講演概要 ]
Instability of volatility parameters in GARCH models in an important issue for analyzing financial time series. In this paper we investigate the asymptotic theory for multiple change point estimators of GARCH$(p,q)$ models. When the parameters of a GARCH models have changed within an observed realization, two types estimators, Maximum likelihood estimator (MLE) and Bayesian estimator (BE), are proposed. Then we derive the asymptotic distributions for these estimators. The MLE and BE have different limit laws, and the BE is asymptotically efficient. Monte Carlo studies on the finite sample behaviors are conducted. Applications to Nikkei 225 index are discussed.
[ 参考URL ]Instability of volatility parameters in GARCH models in an important issue for analyzing financial time series. In this paper we investigate the asymptotic theory for multiple change point estimators of GARCH$(p,q)$ models. When the parameters of a GARCH models have changed within an observed realization, two types estimators, Maximum likelihood estimator (MLE) and Bayesian estimator (BE), are proposed. Then we derive the asymptotic distributions for these estimators. The MLE and BE have different limit laws, and the BE is asymptotically efficient. Monte Carlo studies on the finite sample behaviors are conducted. Applications to Nikkei 225 index are discussed.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/03.html
2009年04月22日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
Arnaud DOUCET 氏 (統計数理研究所)
Interacting Markov chain Monte Carlo Methods for Solving Nonlinear Measure-Valued Equations
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/02.html
Arnaud DOUCET 氏 (統計数理研究所)
Interacting Markov chain Monte Carlo Methods for Solving Nonlinear Measure-Valued Equations
[ 講演概要 ]
We present a new class of interacting Markov chain Monte Carlo algorithms for solving numerically discrete-time measure-valued equations. The associated stochastic processes belong to the class of self-interacting Markov chains. In contrast to traditional Markov chains, their time evolution depend on the occupation measure of their past values. This general methodology allows us to provide a natural way to sample from a sequence of target probability measures of increasing complexity. We develop an original theoretical analysis to analyze the behaviour of these iterative algorithms. We establish a variety of convergence results including exponential estimates and a uniform convergence theorem with respect to the number of target distributions. We also illustrate these algorithms in the context of Feynman-Kac distribution flows.
(this is joint work with Professor Pierre Del Moral)
[ 参考URL ]We present a new class of interacting Markov chain Monte Carlo algorithms for solving numerically discrete-time measure-valued equations. The associated stochastic processes belong to the class of self-interacting Markov chains. In contrast to traditional Markov chains, their time evolution depend on the occupation measure of their past values. This general methodology allows us to provide a natural way to sample from a sequence of target probability measures of increasing complexity. We develop an original theoretical analysis to analyze the behaviour of these iterative algorithms. We establish a variety of convergence results including exponential estimates and a uniform convergence theorem with respect to the number of target distributions. We also illustrate these algorithms in the context of Feynman-Kac distribution flows.
(this is joint work with Professor Pierre Del Moral)
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/02.html
2009年04月15日(水)
16:20-17:30 数理科学研究科棟(駒場) 128号室
Jean JACOD 氏 (Universite Paris VI)
Estimating the successive Blumenthal-Getoor indices for a discretely observed process
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/00.html
Jean JACOD 氏 (Universite Paris VI)
Estimating the successive Blumenthal-Getoor indices for a discretely observed process
[ 講演概要 ]
Letting F be a Levy measure whose "tail" $F ([-x, x])$ admits an expansion $\\sigma_{i\\ge 1} a_i/x^\\beta$ as $x \\rightarrow 0$, we call $\\beta_1 > \\beta_2 >...$ the successive Blumenthal-Getoor indices, since $\\beta_1$ is in this case the usual Blumenthal-Getoor index. This notion may be extended to more general semimartingale. We propose here a method to estimate the $\\beta_i$'s and the coefficients $a_i$'s, or rather their extension for semimartingales, when the underlying semimartingale $X$ is observed at discrete times, on fixed time interval. The asymptotic is when the time-lag goes to $0$. It is then possible to construct consistent estimators for $\\beta_i$ and $a_i$ for those $i$'s such that $\\beta_i > \\beta_1 /2$, whereas it is impossible to do so (even when $X$ is a Levy process) for those $i$'s such that $\\beta_i < \\beta_1 /2$. On the other hand, a central limit theorem for $\\beta_1$ is available only when $\\beta_i < \\beta_1 /2$: consequently, when we can actually consistently estimate some $\\beta_i$'s besides $\\beta_1$ , then no central limit theorem can hold, and correlatively the rates of convergence become quite slow (although one know them explicitly): so the results have some theoretical interest in the sense that they set up bounds on what is actually possible to achieve, but the practical applications are probably quite thin.
(joint with Yacine Ait-Sahalia)
[ 参考URL ]Letting F be a Levy measure whose "tail" $F ([-x, x])$ admits an expansion $\\sigma_{i\\ge 1} a_i/x^\\beta$ as $x \\rightarrow 0$, we call $\\beta_1 > \\beta_2 >...$ the successive Blumenthal-Getoor indices, since $\\beta_1$ is in this case the usual Blumenthal-Getoor index. This notion may be extended to more general semimartingale. We propose here a method to estimate the $\\beta_i$'s and the coefficients $a_i$'s, or rather their extension for semimartingales, when the underlying semimartingale $X$ is observed at discrete times, on fixed time interval. The asymptotic is when the time-lag goes to $0$. It is then possible to construct consistent estimators for $\\beta_i$ and $a_i$ for those $i$'s such that $\\beta_i > \\beta_1 /2$, whereas it is impossible to do so (even when $X$ is a Levy process) for those $i$'s such that $\\beta_i < \\beta_1 /2$. On the other hand, a central limit theorem for $\\beta_1$ is available only when $\\beta_i < \\beta_1 /2$: consequently, when we can actually consistently estimate some $\\beta_i$'s besides $\\beta_1$ , then no central limit theorem can hold, and correlatively the rates of convergence become quite slow (although one know them explicitly): so the results have some theoretical interest in the sense that they set up bounds on what is actually possible to achieve, but the practical applications are probably quite thin.
(joint with Yacine Ait-Sahalia)
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/00.html
2009年04月15日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
Jean JACOD 氏 (Universite Paris VI)
A survey on realized p-variations for semimartingales
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/00.html
Jean JACOD 氏 (Universite Paris VI)
A survey on realized p-variations for semimartingales
[ 講演概要 ]
Let $X$ be a process which is observed at the times $i\\Delta_n$ for $i=0,1,\\ldots,$. If $p>0$ the realized $p$-variation over the time interval $[0, t]$ is
V^n(p)_t=\\sum_{i=1}^{[t/\\Delta_n]}|X_{i\\Delta_n}-X_{(i-1)\\Delta_n}|^p.
The behavior of these $p$-variations when $\\Delta_n ightarrow 0$ (and t is fixed) is now well understood, from the point of view of limits in probability (these are basically old results due to Lepingle) and also for the associated central limit theorem.
The aim of this talk is to review those results, as well as a few extensions (multipower variations, truncated variations). We will put some emphasis on the assumptions on $X$ which are needed, depending on the value of $p$.
[ 参考URL ]Let $X$ be a process which is observed at the times $i\\Delta_n$ for $i=0,1,\\ldots,$. If $p>0$ the realized $p$-variation over the time interval $[0, t]$ is
V^n(p)_t=\\sum_{i=1}^{[t/\\Delta_n]}|X_{i\\Delta_n}-X_{(i-1)\\Delta_n}|^p.
The behavior of these $p$-variations when $\\Delta_n ightarrow 0$ (and t is fixed) is now well understood, from the point of view of limits in probability (these are basically old results due to Lepingle) and also for the associated central limit theorem.
The aim of this talk is to review those results, as well as a few extensions (multipower variations, truncated variations). We will put some emphasis on the assumptions on $X$ which are needed, depending on the value of $p$.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2009/00.html
2009年02月04日(水)
15:00-16:10 数理科学研究科棟(駒場) 128号室
三浦 良造 氏 (一橋大学国際企業戦略研究科)
Non-Parametric Statistics for a partial sums of iid observations: New Trials
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/13.html
三浦 良造 氏 (一橋大学国際企業戦略研究科)
Non-Parametric Statistics for a partial sums of iid observations: New Trials
[ 講演概要 ]
I would lke to review Alpha- quantiles and Ranks with "Empirical Distributions" defined on partial sums of iid. observations discussed in the time continuous version (Brownian Motion).. Then revisiting the formulation of classical estimand and estimators for iid observations , described for example in Fillipova's paper, I would lik,e to discuss on what we could do for our partial sums of iid observations in orde to define our non-parametric estimators and estimands. I will be talking only on the ideas, but mathematical proofs will not be provided.
[ 参考URL ]I would lke to review Alpha- quantiles and Ranks with "Empirical Distributions" defined on partial sums of iid. observations discussed in the time continuous version (Brownian Motion).. Then revisiting the formulation of classical estimand and estimators for iid observations , described for example in Fillipova's paper, I would lik,e to discuss on what we could do for our partial sums of iid observations in orde to define our non-parametric estimators and estimands. I will be talking only on the ideas, but mathematical proofs will not be provided.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/13.html
2009年02月04日(水)
16:20-17:30 数理科学研究科棟(駒場) 128号室
深澤 正彰 氏 (大阪大学 金融・保険教育研究センター)
Black-Scholes 周りの摂動展開について(前半)/ 確率積分の離散化誤差について(後半)
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/14.html
深澤 正彰 氏 (大阪大学 金融・保険教育研究センター)
Black-Scholes 周りの摂動展開について(前半)/ 確率積分の離散化誤差について(後半)
[ 講演概要 ]
(前半)確率ボラティリティモデルに対して知られている, Black-Scholes モデル周りでの各種摂動展開が統一的にマルチンゲール展開の理論によって 厳密に正当化かつ一般化されることを示す. またとくに拡散過程モデルに 対しては再生法を用いてより精密な結果を与える.
(後半)確率積分の近似として, 増大停止時刻列による区間分割 Riemann 和 をとったとき, その近似誤差の漸近分布を与える. ファイナンスへの応用とし てデルタヘッジエラーを解析し, 取引費用を考慮した上で漸近的に平均2乗誤 差を最小化する戦略を定義する.
[ 参考URL ](前半)確率ボラティリティモデルに対して知られている, Black-Scholes モデル周りでの各種摂動展開が統一的にマルチンゲール展開の理論によって 厳密に正当化かつ一般化されることを示す. またとくに拡散過程モデルに 対しては再生法を用いてより精密な結果を与える.
(後半)確率積分の近似として, 増大停止時刻列による区間分割 Riemann 和 をとったとき, その近似誤差の漸近分布を与える. ファイナンスへの応用とし てデルタヘッジエラーを解析し, 取引費用を考慮した上で漸近的に平均2乗誤 差を最小化する戦略を定義する.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/14.html
2009年02月04日(水)
13:40-14:50 数理科学研究科棟(駒場) 128号室
Stefano Maria Iacus 氏 (Universita degli Studi di Milano)
Applications of Iterated Function Systems to Inference and Simulation
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/12.html
Stefano Maria Iacus 氏 (Universita degli Studi di Milano)
Applications of Iterated Function Systems to Inference and Simulation
[ 講演概要 ]
The Iterated Function Systems (IFSs) were born in mid eighties as applications of the theory of discrete dynamical systems and as useful tools for buildings fractals and other similar sets or to produce image compression algorithms. The fundamental result on which the IFS method is based is the Banach contraction theorem because IFSs are defined as operators with some contractive property. In practical applications the crucial point is to solve the inverse problem: given an element f in some metric space (S,d), find a contraction T:S -> S that admits a unique fixed point p such that d(f,p)< eps. When eps=0 the inverse problem is solved exactly and the fixed point p can be identified with the operator T, but in most cases T is an approximation of the target f and T takes linear forms. We present applications of the IFS technique to the problem of estimation of distribution and density functions and to the simulation of L2 stochastic processes.
[ 参考URL ]The Iterated Function Systems (IFSs) were born in mid eighties as applications of the theory of discrete dynamical systems and as useful tools for buildings fractals and other similar sets or to produce image compression algorithms. The fundamental result on which the IFS method is based is the Banach contraction theorem because IFSs are defined as operators with some contractive property. In practical applications the crucial point is to solve the inverse problem: given an element f in some metric space (S,d), find a contraction T:S -> S that admits a unique fixed point p such that d(f,p)< eps. When eps=0 the inverse problem is solved exactly and the fixed point p can be identified with the operator T, but in most cases T is an approximation of the target f and T takes linear forms. We present applications of the IFS technique to the problem of estimation of distribution and density functions and to the simulation of L2 stochastic processes.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/12.html
2008年12月17日(水)
13:40-14:50 数理科学研究科棟(駒場) 002号室
Ilia Negri 氏 (University of Bergamo, Italy)
Goodness of fit tests for ergodic diffusions by discrete sampling schemes
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/09.html
Ilia Negri 氏 (University of Bergamo, Italy)
Goodness of fit tests for ergodic diffusions by discrete sampling schemes
[ 講演概要 ]
We consider a nonparametric goodness of fit test problem for the drift coefficient of one-dimensional ergodic diffusions, where the diffusion coefficient is a nuisance function which is estimated in some sense. Using a theory for the continuous observation case, we construct two kinds of tests based on different types of discrete observations, namely, the data observed discretely in time or in space. We prove that the limit distribution of our tests is the supremum of the standard Brownian motion, and thus our tests are asymptotically distribution free. We also show that our tests are consistent under any fixed alternatives.
joint with Yoichi Nishiyama (Inst. Statist. Math.)
[ 参考URL ]We consider a nonparametric goodness of fit test problem for the drift coefficient of one-dimensional ergodic diffusions, where the diffusion coefficient is a nuisance function which is estimated in some sense. Using a theory for the continuous observation case, we construct two kinds of tests based on different types of discrete observations, namely, the data observed discretely in time or in space. We prove that the limit distribution of our tests is the supremum of the standard Brownian motion, and thus our tests are asymptotically distribution free. We also show that our tests are consistent under any fixed alternatives.
joint with Yoichi Nishiyama (Inst. Statist. Math.)
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/09.html
2008年12月17日(水)
15:00-16:10 数理科学研究科棟(駒場) 002号室
Stefano Maria Iacus 氏 (Universita degli Studi di Milano, Italy)
Divergences Test Statistics for Discretely Observed Diffusion Processes
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/10.html
Stefano Maria Iacus 氏 (Universita degli Studi di Milano, Italy)
Divergences Test Statistics for Discretely Observed Diffusion Processes
[ 講演概要 ]
In this paper we propose the use of $\\phi$-divergences as test statistics to verify simple hypotheses about a one-dimensional parametric diffusion process dXt = b(Xt, theta)dt + sigma(Xt, theta) dWt, from discrete observations at times ti = i*Dn, i=0, 1, ..., n, under the asymptotic scheme Dn - 0, n*Dn - +oo and n*Dn^2 - 0. The class of phi-divergences is wide and includes several special members like Kullback-Leibler, Renyi, power and alpha-divergences. We derive the asymptotic distribution of the test statistics based on phi- divergences. The limiting law takes different forms depending on the regularity of phi. These convergence differ from the classical results for independent and identically distributed random variables. Numerical analysis is used to show the small sample properties of the test statistics in terms of estimated level and power of the test.
joint work with A. De Gregorio
[ 参考URL ]In this paper we propose the use of $\\phi$-divergences as test statistics to verify simple hypotheses about a one-dimensional parametric diffusion process dXt = b(Xt, theta)dt + sigma(Xt, theta) dWt, from discrete observations at times ti = i*Dn, i=0, 1, ..., n, under the asymptotic scheme Dn - 0, n*Dn - +oo and n*Dn^2 - 0. The class of phi-divergences is wide and includes several special members like Kullback-Leibler, Renyi, power and alpha-divergences. We derive the asymptotic distribution of the test statistics based on phi- divergences. The limiting law takes different forms depending on the regularity of phi. These convergence differ from the classical results for independent and identically distributed random variables. Numerical analysis is used to show the small sample properties of the test statistics in terms of estimated level and power of the test.
joint work with A. De Gregorio
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/10.html
2008年12月17日(水)
16:20-17:30 数理科学研究科棟(駒場) 002号室
Nicolas Privault 氏 (City University of Hong Kong)
Stein estimation of Poisson process intensities
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/11.html
Nicolas Privault 氏 (City University of Hong Kong)
Stein estimation of Poisson process intensities
[ 講演概要 ]
In this talk we will construct superefficient estimators of Stein type for the intensity parameter lambda > 0 of a Poisson process, using integration by parts and superharmonic functionals on the Poisson space.
[ 参考URL ]In this talk we will construct superefficient estimators of Stein type for the intensity parameter lambda > 0 of a Poisson process, using integration by parts and superharmonic functionals on the Poisson space.
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/11.html
2008年10月30日(木)
16:20-17:30 数理科学研究科棟(駒場) 270号室
若木 宏文 氏 (広島大学大学院理学研究科)
正規母集団に関する検定統計量の分布の漸近展開の誤差評価
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/08.html
若木 宏文 氏 (広島大学大学院理学研究科)
正規母集団に関する検定統計量の分布の漸近展開の誤差評価
[ 講演概要 ]
多変量正規母集団に関する検定統計量の帰無分布の特性関数は、いくつかの多変量ガ ンマ関数の比として表されることが多い。Box (1949) はガンマ関数の漸近展開(ス ターリングの公式)を用いて、このような分布の大標本漸近展開公式を導出したが、 導出過程での剰余項を詳しく評価してゆくことで、分布関数の漸近展開近似の計算可 能な誤差限界を導出することができる。いくつかの検定統計量に対する大標本漸近展 開近似の誤差限界を導出し、高次元・大標本の枠組みで得られているエッジワース展 開近似の誤差限界と比較する。
[ 参考URL ]多変量正規母集団に関する検定統計量の帰無分布の特性関数は、いくつかの多変量ガ ンマ関数の比として表されることが多い。Box (1949) はガンマ関数の漸近展開(ス ターリングの公式)を用いて、このような分布の大標本漸近展開公式を導出したが、 導出過程での剰余項を詳しく評価してゆくことで、分布関数の漸近展開近似の計算可 能な誤差限界を導出することができる。いくつかの検定統計量に対する大標本漸近展 開近似の誤差限界を導出し、高次元・大標本の枠組みで得られているエッジワース展 開近似の誤差限界と比較する。
https://www.ms.u-tokyo.ac.jp/~kengok/statseminar/2008/08.html
