Accession Number : ADA324872

Title :   Model Selection with Data-Oriented Penalty.

Descriptive Note : Technical rept.,

Corporate Author : PENNSYLVANIA STATE UNIV UNIVERSITY PARK CENTER FOR MULTIVARIATE ANALYSIS

Personal Author(s) : Bai, Z. D. ; Rao, C. R. ; Wu, Y.

PDF Url : ADA324872

Report Date : APR 1997

Pagination or Media Count : 25

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).

Descriptors :   *MATHEMATICAL MODELS, *LINEAR REGRESSION ANALYSIS, DATA MANAGEMENT, PROBABILITY DISTRIBUTION FUNCTIONS, RANDOM VARIABLES, EIGENVALUES, MONTE CARLO METHOD, STATISTICAL SAMPLES.

Subject Categories : Statistics and Probability
      Operations Research

Distribution Statement : APPROVED FOR PUBLIC RELEASE