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A weak‐signal‐assisted procedure for variable selection and statistical inference with an informative subsample
Author(s) -
Fang Fang,
Zhao Jiwei,
Ahmed S. Ejaz,
Qu Annie
Publication year - 2021
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13346
Subject(s) - pairwise comparison , inference , estimator , computer science , statistical inference , selection (genetic algorithm) , feature selection , variable (mathematics) , latent variable , statistical hypothesis testing , statistics , machine learning , artificial intelligence , econometrics , data mining , mathematics , mathematical analysis
This paper is motivated from an HIV‐1 drug resistance study where we encounter three analytical challenges: to analyze data with an informative subsample, to take into account the weak signals, and to detect important signals and also conduct statistical inference. We start with an initial estimation method, which adopts a penalized pairwise conditional likelihood approach for variable selection. This initial estimator incorporates the informative subsample issue. To accounting for the effect of weak signals, we use a key idea of partial ridge regression. We also propose a one‐step estimation method for each of the signal coefficients and then construct confidence intervals accordingly. We apply the proposed method to the Stanford HIV‐1 drug resistance study and compare the results with existing approaches. We also conduct comprehensive simulation studies to demonstrate the superior performance of our proposed method.