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Testing and Confidence Intervals for High Dimensional Proportional Hazards Model.

ABSTRACT: This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Without assuming model selection consistency, we prove the asymptotic normality of these test statistics, establish their semiparametric optimality. We also develop new procedures for constructing pointwise confidence intervals for the baseline hazard function and baseline survival function. Thorough numerical results are provided to back up our theory.

SUBMITTER: Fang EX

PROVIDER: S-EPMC10584375 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Testing and Confidence Intervals for High Dimensional Proportional Hazards Model.

Fang Ethan X EX Ning Yang Y Liu Han H

Journal of the Royal Statistical Society. Series B, Statistical methodology 20161226 5

This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Without assuming model selection consistency, we prove the asymptotic normality of these test statistics, establish their semiparametric optimality. We also develop new procedures fo ...[more]

PMID: 37854943

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