Testing covariates in high-dimensional regression |
| |
Authors: | Wei Lan Hansheng Wang Chih-Ling Tsai |
| |
Institution: | 1. Statistics School, Southwestern University of Finance and Economics, Chengdu, Sichuan, 610074, People’s Republic of China 2. Guanghua School of Management, Peking University, Beijing, 100871, People’s Republic of China 3. Graduate School of Management, University of California–Davis, Davis, CA, 95616, USA
|
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|