Model Selection with Data-Oriented Penalty.

Abstract

We consider the model selection or variables selection in the classical regression problem. In the literature, there are two types of criteria for model selection, one based on prediction error (FPE) and another on information theoretic considerations (GIC). Each of these criteria uses a certain penalty function which is the product of the number of variables j in a submodel and a function C(n) depending on n and satisfying some conditions to guarantee consistency in model selection. One of the important problems in such a procedure is the actual choice of C(n) in a given situation. In this paper we show that a particular choice of C(n) based on observed data, which makes it random, preserves the consistency property and shows improved performance over a fixed choice of C(n).

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1997
Accession Number
ADA324872

Entities

People

  • Calyampudi Radhakrishna Rao
  • Yipeng Wu
  • Z. D. Bai

Organizations

  • Pennsylvania State University

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Fields of Study

  • Mathematics

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  • Regression Analysis.
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