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A new criterion for variable selection

โœ Scribed by R. Philips; I. Guttman

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
493 KB
Volume
38
Category
Article
ISSN
0167-7152

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โœฆ Synopsis

The variable/model selection problem is reexamined from a Bayesian perspective using data splitting to establish a joint prior for the relevent parameters. This allows for the required integrations that have to be performed to be over the same dimensional parameter space. It also produces a result which is independent of the scaling of both the independent as well as dependent variables. The posterior probability of each model .J#~ is calculated, where the subscript cยข is used to index the subsets of the predictor variables. This probability is shown to be asymptotically equal to 1, if +J#~ is the correct model. A new model selection criterion is also derived from this expression. Examples using simulated data and real data sets are provided.

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โœ Chih-Yuan Tseng ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 159 KB

Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other