Linear regression model selection using p-values when the model dimension grows
Statistics Theory
2012-05-21 v1 Statistics Theory
Abstract
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied for families of linear regression models. We prove that such procedures are consistent i.e. the minimal true model is chosen with probability tending to 1 even when the number of models under consideration slowly increases with a sample size. The simulation study indicates that introduced methods perform promisingly when compared with Akaike and Bayesian Information Criteria.
Cite
@article{arxiv.1205.4146,
title = {Linear regression model selection using p-values when the model dimension grows},
author = {Piotr Pokarowski and Jan Mielniczuk and Paweł Teisseyre},
journal= {arXiv preprint arXiv:1205.4146},
year = {2012}
}