A new criterion for variable selection

R. Philips and I. Guttman

Statistics & Probability Letters, 1998, vol. 38, issue 1, 11-19

Abstract: 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 [infinity] is calculated, where the subscript [alpha] is used to index the subsets of the predictor variables. This probability is shown to be asymptotically equal to 1, if [alpha] 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.

Keywords: Variable; selection; Model; selection; Posterior; probability; Data; splitting; prior (search for similar items in EconPapers)
Date: 1998
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(97)00148-X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:38:y:1998:i:1:p:11-19

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().