Functional Choice and Non-significance Regions in Regression
Statistics Theory
2016-05-09 v1 Statistics Theory
Abstract
Given data and covariates the problem is to decide which covariates to include when approximating by a linear function of the covariates. The decision is based on replacing subsets of the covariates by i.i.d. normal random variables and comparing the error with that obtained by retaining the subsets. If the two errors are not significantly different for a particular subset it is concluded that the covariates in this subset are no better than random noise and they are not included in the linear approximation to .
Keywords
Cite
@article{arxiv.1605.01936,
title = {Functional Choice and Non-significance Regions in Regression},
author = {Laurie Davies},
journal= {arXiv preprint arXiv:1605.01936},
year = {2016}
}