Estimation and variable selection for semiparametric transformation models under a more efficient cohort sampling design

Mingzhe Wu, Ming Zheng, Wen Yu () and Ruofan Wu
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Mingzhe Wu: Fudan University
Ming Zheng: Fudan University
Wen Yu: Fudan University
Ruofan Wu: Fudan University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2018, vol. 27, issue 3, No 6, 570-596

Abstract: Abstract Two-phase cohort sampling designs, or sometimes known as retrospective sampling designs, are often used in large cohort studies for saving sampling time and cost. Commonly used designs include case–cohort design, case–control design, nested case–control design, and so on. Efforts had been taken to improve the estimation efficiency under these commonly used designs. We propose a different retrospective sampling design, called end-point design, under the class of semiparametric transformation models. An inverse probability weighting likelihood approach is designed for estimating the model parameters, and the proposed design shows higher efficiency than the case–cohort and case–control design with comparable size of covariates ascertainment. We also consider variable selection under the proposed design. A specially designed objective function with adaptive lasso penalty is proposed. The large sample properties of the proposed estimation and variable selection procedure are developed. Extensive simulation studies are carried out to show favorable evidence for the proposed approaches. A real data set is analyzed for illustration.

Keywords: Adaptive lasso penalty; Case–cohort sampling; Efficiency improvement; Inverse probability weighting; Retrospective sampling; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11749-017-0562-2

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