Efficient test-based variable selection for high-dimensional linear models.

TitleEfficient test-based variable selection for high-dimensional linear models.
Publication TypeJournal Article
Year of Publication2018
AuthorsGong, Siliang, Kai Zhang, and Yufeng Liu
JournalJ Multivar Anal
Date Published2018 Jul

Variable selection plays a fundamental role in high-dimensional data analysis. Various methods have been developed for variable selection in recent years. Well-known examples are forward stepwise regression (FSR) and least angle regression (LARS), among others. These methods typically add variables into the model one by one. For such selection procedures, it is crucial to find a stopping criterion that controls model complexity. One of the most commonly used techniques to this end is cross-validation (CV) which, in spite of its popularity, has two major drawbacks: expensive computational cost and lack of statistical interpretation. To overcome these drawbacks, we introduce a flexible and efficient test-based variable selection approach that can be incorporated into any sequential selection procedure. The test, which is on the overall signal in the remaining inactive variables, is based on the maximal absolute partial correlation between the inactive variables and the response given active variables. We develop the asymptotic null distribution of the proposed test statistic as the dimension tends to infinity uniformly in the sample size. We also show that the test is consistent. With this test, at each step of the selection, a new variable is included if and only if the -value is below some pre-defined level. Numerical studies show that the proposed method delivers very competitive performance in terms of variable selection accuracy and computational complexity compared to CV.

Alternate JournalJ Multivar Anal
Original PublicationEfficient test-based variable selection for high-dimensional linear models.
PubMed ID30613114
PubMed Central IDPMC6317739
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P50 DA039838 / DA / NIDA NIH HHS / United States
R01 GM126550 / GM / NIGMS NIH HHS / United States