Bayesian variable selection for the seemingly unrelated regression model with a large number of predictors

Material type: 文書・図像類

Author: 安道, 知寛

Publisher: 慶應義塾経営管理学会

Publication date: 2010-08

Material Format: Digital

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Note (General)：: type:textComputationally efficient methods for Bayesian analysis of Seemingly unrelated regression (SCR) models are deYeloped. Cnder a Bayesian hierar...

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Material Type: 文書・図像類
Title: Bayesian variable selection for the seemingly unrelated regression model with a large number of predictors
Author/Editor: 安道, 知寛
Author Heading: 安道, 知寛
Publication, Distribution, etc.: 慶應義塾経営管理学会
Publication Date: 2010-08
Publication Date (W3CDTF): 2010-08
Periodical title: 慶應義塾経営管理学会リサーチペーパー・シリーズ
No. or year of volume/issue: 103
Volume: 103
Text Language Code: eng
Subject Heading: Bayesian estimation,Seerningly Cnrela.ted Regression,Direct Monte Carlo,Markov Chain Monte Carlo
Target Audience: 一般
Note (General): type:text
Computationally efficient methods for Bayesian analysis of Seemingly unrelated regression (SCR) models are deYeloped. Cnder a Bayesian hierarchical framework where ea.ch regression function is represented as a linear combination of a large number of basis functions, the regression coefficients, the variance matrix of the errors, and a set of variables to be included in the model are estimated simultaneously. Usually the Bayesian estimation problem is solved using Markov Chain Monte Carlo (MCMC) techniques. Herein we shew how a direct Monte Carlo (DMC) technique can be employed to solve this estimation problem more efficiently.''
Source: https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/download.php?koara_id=KO40003002-00000103-0001
https://koara.lib.keio.ac.jp/xoonips/modules/xoonips/download.php?koara_id=KO40003002-00000103-0001
Access Restrictions: 限定公開
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 慶應義塾大学 : 慶應義塾大学学術情報リポジトリ