A Beyseian Detection for the Number of Change Points in Linear Regression Model

碩士 === 國立中興大學 === 統計學研究所 === 102 === A Bayesian approach is considered to detect the number of change points in linear regression model. The work is the extension of that given by Fan et al. (1996) for simple linear regression. The normal-gamma prior information for the regression parameters is empl...

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Main Authors:	Chia-Yi Liao, 廖家儀
Other Authors:	Chung-Bow Lee
Format:	Others
Language:	en_US
Published:	2014
Online Access:	http://ndltd.ncl.edu.tw/handle/26052644698619509944

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spelling	ndltd-TW-102NCHU53370072016-09-24T04:07:23Z http://ndltd.ncl.edu.tw/handle/26052644698619509944 A Beyseian Detection for the Number of Change Points in Linear Regression Model 利用貝氏方法偵測改變點個數於線性迴歸模型 Chia-Yi Liao 廖家儀碩士國立中興大學統計學研究所 102 A Bayesian approach is considered to detect the number of change points in linear regression model. The work is the extension of that given by Fan et al. (1996) for simple linear regression. The normal-gamma prior information for the regression parameters is employed in the analysis. The marginal posterior distribution of the location of change points and the number of change points are derived. Under mild assumptions, consistency for the number of change points and boundedness between the posterior mode of the location and true location of change points are also estab- lished. The Bayesian approach for the detection of the number of change points is suitable whether the switching linear regression is continuous or discontinuous. Some simulated results are given. Chung-Bow Lee 李宗寶 2014 學位論文 ; thesis 35 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立中興大學 === 統計學研究所 === 102 === A Bayesian approach is considered to detect the number of change points in linear regression model. The work is the extension of that given by Fan et al. (1996) for simple linear regression. The normal-gamma prior information for the regression parameters is employed in the analysis. The marginal posterior distribution of the location of change points and the number of change points are derived. Under mild assumptions, consistency for the number of change points and boundedness between the posterior mode of the location and true location of change points are also estab- lished. The Bayesian approach for the detection of the number of change points is suitable whether the switching linear regression is continuous or discontinuous. Some simulated results are given.
author2	Chung-Bow Lee
author_facet	Chung-Bow Lee Chia-Yi Liao 廖家儀
author	Chia-Yi Liao 廖家儀
spellingShingle	Chia-Yi Liao 廖家儀 A Beyseian Detection for the Number of Change Points in Linear Regression Model
author_sort	Chia-Yi Liao
title	A Beyseian Detection for the Number of Change Points in Linear Regression Model
title_short	A Beyseian Detection for the Number of Change Points in Linear Regression Model
title_full	A Beyseian Detection for the Number of Change Points in Linear Regression Model
title_fullStr	A Beyseian Detection for the Number of Change Points in Linear Regression Model
title_full_unstemmed	A Beyseian Detection for the Number of Change Points in Linear Regression Model
title_sort	beyseian detection for the number of change points in linear regression model
publishDate	2014
url	http://ndltd.ncl.edu.tw/handle/26052644698619509944
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A Beyseian Detection for the Number of Change Points in Linear Regression Model

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