On selecting a power transformation in time-series analysis
| dc.citation.epage | 354 | en_US |
| dc.citation.issue | 5 | en_US |
| dc.citation.spage | 343 | en_US |
| dc.citation.volume | 16 | en_US |
| dc.citation.woscount | 2 | |
| dc.contributor.author | Chen, CWS | en_US |
| dc.contributor.author | Lee, JC | en_US |
| dc.contributor.department | 交大名義發表 | zh_TW |
| dc.contributor.department | National Chiao Tung University | 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.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.identifier.issn | 0277-6693 | en_US |
| dc.identifier.journal | JOURNAL OF FORECASTING | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/14555 | |
| dc.identifier.wosnumber | WOS:A1997YD78100005 | |
| 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 |