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統計数学セミナー
過去の記録 ～04/30｜次回の予定｜今後の予定 05/01～
| 担当者 | 吉田朋広、増田弘毅、荻原哲平、小池祐太 |
|---|---|
| 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. |
次回の予定
2025年05月02日(金)
13:30-14:30 数理科学研究科棟(駒場) 126号室
ハイブリッド開催、ベイズ計算セミナーとの共催
今井 竣祐 氏 (京大経済)
General Bayesian Semiparametric Inference with Hyvärinen Score (Japanese)
https://us06web.zoom.us/meeting/register/3XxtsHwaQVSN7BuINu6E8g
ハイブリッド開催、ベイズ計算セミナーとの共催
今井 竣祐 氏 (京大経済)
General Bayesian Semiparametric Inference with Hyvärinen Score (Japanese)
[ 講演概要 ]
This paper proposes a novel framework for semiparametric Bayesian inference on finite-dimensional parameters under existence of nuisance functions. Based on a pseudo-model defined by (profiled) loss functions for the finite dimensional parameters and the Hyv\"arinen score, we propose a general posterior distribution, named semiparametric Hyv\"arinen (SH) posterior. The SH posterior enables us to make inference on the parameters of interest with taking account of uncertainty in the estimation/selection of tuning parameters in estimating the unknown nuisance functions. We establish its theoretical justification of the SH posterior under large samples, and provide posterior computation algorithm. As concrete examples, we provide the posterior inference of partial linear models and single index models, and demonstrate the performance through simulation.
[ 参考URL ]This paper proposes a novel framework for semiparametric Bayesian inference on finite-dimensional parameters under existence of nuisance functions. Based on a pseudo-model defined by (profiled) loss functions for the finite dimensional parameters and the Hyv\"arinen score, we propose a general posterior distribution, named semiparametric Hyv\"arinen (SH) posterior. The SH posterior enables us to make inference on the parameters of interest with taking account of uncertainty in the estimation/selection of tuning parameters in estimating the unknown nuisance functions. We establish its theoretical justification of the SH posterior under large samples, and provide posterior computation algorithm. As concrete examples, we provide the posterior inference of partial linear models and single index models, and demonstrate the performance through simulation.
https://us06web.zoom.us/meeting/register/3XxtsHwaQVSN7BuINu6E8g
