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Model Selection and Regression t-Statistics

It is shown that dropping quantitative variables from a linear regression, based on t-statistics, is mathematically equivalent to dropping variables based on commonly used information criteria.

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Published in:	The American statistician 2018-10, Vol.72 (4), p.379-381
Main Authors:	Derryberry, DeWayne, Aho, Ken, Edwards, John, Peterson, Teri
Format:	Article
Language:	English
Subjects:	Information criteria Linear regression Model selection Regression analysis Regression models SHORT TECHNICAL NOTES Statistical methods Statistics t-Statistics
Citations:	Items that this one cites Items that cite this one
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description	It is shown that dropping quantitative variables from a linear regression, based on t-statistics, is mathematically equivalent to dropping variables based on commonly used information criteria.
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source	JSTOR Archival Journals and Primary Sources Collection; Taylor and Francis Science and Technology Collection
subjects	Information criteria Linear regression Model selection Regression analysis Regression models SHORT TECHNICAL NOTES Statistical methods Statistics t-Statistics
title	Model Selection and Regression t-Statistics
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