On selecting a power transformation in time-series analysis

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DC Field	Value	Language
dc.contributor.author	Chen, CWS	en_US
dc.contributor.author	Lee, JC	en_US
dc.date.accessioned	2014-12-08T15:20:28Z	-
dc.date.available	2014-12-08T15:20:28Z	-
dc.date.issued	1997-09-01	en_US
dc.identifier.issn	0277-6693	en_US
dc.identifier.uri	http://hdl.handle.net/11536/14555	-
dc.description.abstract	The primary aim of this paper is to select an appropriate power transformation when we use ARMA models for a given time series. We propose a Bayesian procedure for estimating the power transformation as well as other parameters in time series models. The posterior distributions of interest are obtained utilizing the Gibbs sampler, a Markov Chain Monte Carlo (MCMC) method. The proposed methodology is illustrated with two real data sets. The performance of the proposed procedure is compared with other competing procedures. (C) 1997 John Wiley & Sons, Ltd.	en_US
dc.language.iso	en_US	en_US
dc.subject	ARMA models	en_US
dc.subject	Forecasting	en_US
dc.subject	Gibbs sampler	en_US
dc.subject	MCMC method	en_US
dc.subject	power transformation	en_US
dc.title	On selecting a power transformation in time-series analysis	en_US
dc.type	Article	en_US
dc.identifier.journal	JOURNAL OF FORECASTING	en_US
dc.citation.volume	16	en_US
dc.citation.issue	5	en_US
dc.citation.spage	343	en_US
dc.citation.epage	354	en_US
dc.contributor.department	交大名義發表	zh_TW
dc.contributor.department	National Chiao Tung University	en_US
dc.identifier.wosnumber	WOS:A1997YD78100005	-
dc.citation.woscount	2	-
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