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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. |
2020年05月01日(金)
17:00-18:10 数理科学研究科棟(駒場) φ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。
Xiao Fang 氏 (Chinese University of Hong Kong)
High order distributional approximations by Stein's method (ENGLISH)
https://docs.google.com/forms/d/e/1FAIpQLSeSVwYsjhyQQXzjt3ZpvRh9ZEO5qZXxxLxYDYOu301Mc89RCA/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。
Xiao Fang 氏 (Chinese University of Hong Kong)
High order distributional approximations by Stein's method (ENGLISH)
[ 講演概要 ]
Stein's method is a powerful tool to proving distributional approximations with error bounds. In this talk, we present two recent developments of Stein's method for high order approximations. (1) Together with Li Luo and Qi-Man Shao, we consider skewness correction in normal approximation. We prove a refined Cram¥'er-type moderate deviation result for a class of statistics possessing a local structure. We discuss applications to k-runs, U-statistics and subgraph counts. (2) Together with Anton Braverman and Jim Dai, we derive and justify new diffusion approximations with state-dependent diffusion coefficients for stationary distributions of Markov chains. We discuss applications to the Erlang-C system, a hospital inpatient flow model and the auto-regressive model.
[ 参考URL ]Stein's method is a powerful tool to proving distributional approximations with error bounds. In this talk, we present two recent developments of Stein's method for high order approximations. (1) Together with Li Luo and Qi-Man Shao, we consider skewness correction in normal approximation. We prove a refined Cram¥'er-type moderate deviation result for a class of statistics possessing a local structure. We discuss applications to k-runs, U-statistics and subgraph counts. (2) Together with Anton Braverman and Jim Dai, we derive and justify new diffusion approximations with state-dependent diffusion coefficients for stationary distributions of Markov chains. We discuss applications to the Erlang-C system, a hospital inpatient flow model and the auto-regressive model.
https://docs.google.com/forms/d/e/1FAIpQLSeSVwYsjhyQQXzjt3ZpvRh9ZEO5qZXxxLxYDYOu301Mc89RCA/viewform
