Testing covariates in high-dimensional regression

Wei Lan, Hansheng Wang () and Chih-Ling Tsai

Annals of the Institute of Statistical Mathematics, 2014, vol. 66, issue 2, 279-301

Abstract: In a high-dimensional linear regression model, we propose a new procedure for testing statistical significance of a subset of regression coefficients. Specifically, we employ the partial covariances between the response variable and the tested covariates to obtain a test statistic. The resulting test is applicable even if the predictor dimension is much larger than the sample size. Under the null hypothesis, together with boundedness and moment conditions on the predictors, we show that the proposed test statistic is asymptotically standard normal, which is further supported by Monte Carlo experiments. A similar test can be extended to generalized linear models. The practical usefulness of the test is illustrated via an empirical example on paid search advertising. Copyright The Institute of Statistical Mathematics, Tokyo 2014

Keywords: Generalized linear model; High-dimensional data; Hypotheses testing; Paid search advertising; Partial covariance; Partial F-test (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10463-013-0414-0

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