The Annals of Statistics

Hypothesis testing for high-dimensional sparse binary regression

Rajarshi Mukherjee, Natesh S. Pillai, and Xihong Lin

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Abstract

In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies.

Article information

Source
Ann. Statist., Volume 43, Number 1 (2015), 352-381.

Dates
First available in Project Euclid: 6 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.aos/1423230083

Digital Object Identifier
doi:10.1214/14-AOS1279

Mathematical Reviews number (MathSciNet)
MR3311863

Zentralblatt MATH identifier
1308.62094

Subjects
Primary: 62G10: Hypothesis testing 62G20: Asymptotic properties 62C20: Minimax procedures

Keywords
Minimax hypothesis testing binary regression detection boundary Higher Criticism sparsity

Citation

Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong. Hypothesis testing for high-dimensional sparse binary regression. Ann. Statist. 43 (2015), no. 1, 352--381. doi:10.1214/14-AOS1279. https://projecteuclid.org/euclid.aos/1423230083


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Supplemental materials

  • Supplementary material: Supplement to “Hypothesis testing for high-dimensional sparse binary regression”. The supplementary material contain the proofs of all theorems, propositions and supporting lemmas.