A new algorithm in bayesian model averaging in regression models

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/109128

Title:	A new algorithm in bayesian model averaging in regression models
Authors:	于振華;Fan, Tsai-Hung;Wang, Guo-Tzau;Yu, Jenn-Hwa
Contributors:	理學院數學系
Keywords:	Algorithms;Ayesian model averaging;Bayesian analysis;Computational efficiency;Computer simulation;Computing time;Mathematical models;Occam's window;Primary 62F15;Regression;Regression models;Secondary 65C05;Walking
Date:	2014-01-01
Issue Date:	2026-04-23 16:09:32 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Philadelphia: Taylor & Francis Group
Abstract:	摘要： We propose a new iterative algorithm, namely the model walking algorithm, to modify the widely used Occam's window method in Bayesian model averaging procedure. It is verified, by simulation, that in the regression models, the proposed algorithm is much more efficient in terms of computing time and the selected candidate models. Moreover, it is not sensitive to the initial models. 出版者： Philadelphia: Taylor & Francis Group 出版日期： 2014-01-01 出處： Communications in statistics. Simulation and computation, 2014-01, Vol.43 (2), p.315-328 資源來源： Taylor & Francis Journals Auto-Holdings Collection 版權： Copyright Taylor & Francis Group, LLC 2014 版權： Copyright Taylor and Francis Group, LLC 識別號： ISSN: 0361-0918 識別號： EISSN: 1532-4141 識別號： DOI: 10.1080/03610918.2012.700750
Appears in Collections:	[Department of Mathematics] journal & Dissertation

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