Testing for the presence of significant covariates through conditional marginal regression

Yanlin Tang, Huixia Judy Wang, Emre Barut

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Researchers sometimes have a priori information on the relative importance of predictors that can be used to screen out covariates. An important question is whether any of the discarded covariates have predictive power when the most relevant predictors are included in the model. We consider testing whether any discarded covariate is significant conditional on some pre-chosen covariates. We propose a maximum-type test statistic and show that it has a nonstandard asymptotic distribution, giving rise to the conditional adaptive resampling test. To accommodate signals of unknown sparsity, we develop a hybrid test statistic, which is a weighted average of maximum- and sum-type statistics. We prove the consistency of the test procedure under general assumptions and illustrate how it can be used as a stopping rule in forward regression. We show, through simulation, that the proposed method provides adequate control of the familywise error rate with competitive power for both sparse and dense signals, even in high-dimensional cases, and we demonstrate its advantages in cases where the covariates are heavily correlated. We illustrate the application of our method by analysing an expression quantitative trait locus dataset.
Original languageEnglish (US)
Pages (from-to)57-71
Number of pages15
Issue number1
StatePublished - Dec 8 2017
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2021-04-06
Acknowledgements: The authors thank the reviewers, associate editor and editor for constructive comments and helpful suggestions. The authors also thank Professor Soumendra Lahiri for helpful discussions. This research was supported by the U.S. National Science Foundation, the National Natural Science Foundation of China, and the King Abdullah University of Science & Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • General Mathematics
  • Agricultural and Biological Sciences (miscellaneous)


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