Model selection with data oriented penalty

Bai, Z. D. ; Rao, C. R. ; Wu, Y. (1999) Model selection with data oriented penalty Journal of Statistical Planning and Inference, 77 (1). pp. 103-117. ISSN 0378-3758

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We consider the problem of model (or variable) selection in the classical regression model using the GIC (general information criterion). In this method the maximum likelihood is used with a penalty function denoted by Cn, depending on the sample size n and chosen to ensure consistency in the selection of the true model. There are various choices of Cn suggested in the literature on model selection. In this paper we show that a particular choice of Cn based on observed data, which makes it random, preserves the consistency property and provides improved performance over a fixed choice of Cn.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
Keywords:AIC; GIC; Linear Regression; Model Selection; Variables Selection
ID Code:71899
Deposited On:28 Nov 2011 04:20
Last Modified:28 Nov 2011 04:20

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