 Regression model selection—a residual likelihood approachPeide Shi and Chih‐Ling Tsai Journal of the Royal Statistical Society Series B, 2002, vol. 64, issue 2, 237-252 Abstract: Summary. We obtain the residual information criterion RIC, a selection criterion based on the residual log‐likelihood, for regression models including classical regression models, Box–Cox transformation models, weighted regression models and regression models with autoregressive moving average errors. We show that RIC is a consistent criterion, and that simulation studies for each of the four models indicate that RIC provides better model order choices than the Akaike information criterion, corrected Akaike information criterion, final prediction error, Cp and Radj2, except when the sample size is small and the signal‐to‐noise ratio is weak. In this case, none of the criteria performs well. Monte Carlo results also show that RIC is superior to the consistent Bayesian information criterion BIC when the signal‐to‐noise ratio is not weak, and it is comparable with BIC when the signal‐to‐noise ratio is weak and the sample size is large. Date: 2002 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:64:y:2002:i:2:p:237-252 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC. |
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