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＜電子ブック＞
Parametric Statistical Change Point Analysis / by Jie Chen, A.K. Gupta

版	1st ed. 2000.
出版者	(Boston, MA : Birkhäuser Boston : Imprint: Birkhäuser)
出版年	2000
本文言語	英語
大きさ	VIII, 184 p : online resource
著者標目	*Chen, Jie author Gupta, A.K author SpringerLink (Online service)
件　名	LCSH:Statistics  FREE:Statistical Theory and Methods
一般注記	1 Preliminaries -- 2 Univariate Normal Model -- 3 Multivariate Normal Model -- 4 Regression Models -- 5 Gamma Model -- 6 Exponential Model -- 7 Discrete Models -- Author Index Recently there has been a keen interest in the statistical analysis of change point detec­ tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera­ ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de­ cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol­ lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres­ sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential HTTP:URL=https://doi.org/10.1007/978-1-4757-3131-6

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