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Activity Number: 532
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #318824 View Presentation
Title: Efficient Semiparametric Inference Under Two-Phase Sampling, with Applications to Genetic Association Studies
Author(s): Ran Tao* and Donglin Zeng and Danyu Lin
Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
Keywords: Biased sampling ; EM algorithm ; Genome sequencing ; Response-selective sampling ; Semiparametric efficiency ; Sieve approximation

In modern epidemiological and medical studies, the covariates of interest may involve genome sequencing or biomarker assay and thus are prohibitively expensive to measure on a large number of subjects. A cost-effective solution is the two-phase design, under which the outcome and inexpensive covariates are observed for all subjects during the first phase and that information is used to select subjects for measurements of expensive covariates during the second phase. Herein, we consider general two-phase designs, where the outcome can be continuous or discrete, and inexpensive covariates can be continuous and correlated with expensive covariates. We propose a semiparametric approach to regression analysis by approximating the conditional density functions of expensive covariates given inexpensive covariates with B-spline sieves. We devise a computationally efficient and numerically stable EM-algorithm and establish the consistency, asymptotic normality, and asymptotic efficiency of the resulting estimators. We demonstrate the superiority of the proposed methods over existing ones through extensive simulation studies. We present applications to the aforementioned NHLBI ESP.

Authors who are presenting talks have a * after their name.

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