On Information Criteria in Linear Regression Model

Best Regression Model Using Information Criteria

Consistency Properties of Model Selection Criteria in Multiple Linear Regression

This paper concerns the asymptotic properties of a class of criteria for model selection in linear regression models, which covers the most well known criteria as e.g. MALLOWS' Cp, CV (cross-validation), GCV ( generalized cross-validation), AKAIKE's AIC and FPE as well as SCHWARZ' BIC. These criteria are shown to be consistent in the sense of selecting the true or larger models, assuming i.i.d....

Stein Type Estimators for Disturbance Variance in Linear Regression Model

This article has no abstract.

Criteria for Linear Model

Model selection criteria frequently arise from constructing estimators of discrepancy measures used to assess the disparity between thètrue' model and a tted approximating model. The Akaike (1973) information criterion and its variants result from utilizing Kullback's (1968) directed divergence as the targeted discrepancy. The directed divergence is an asym-metric measure of separation between ...

An Exact Implicit Enumeration Algorithm for Variable Selection in Multiple Linear Regression Models Using Information Criteria

For large multivariate data sets the data analyst often wants to know the best set of independent regressors to use in a multiple linear regression model. Akaike’s Information Criteria (AIC) is one information criterion calculated in SAS that is used to score a model. For a small number of independent variables p, an explicit enumeration of all possible 2 models is possible. However, for large ...