期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
卷 82, 期 2, 页码 445-465出版社
WILEY
DOI: 10.1111/rssb.12354
关键词
Data integration; Double robustness; Generalizability; Penalized estimating equation; Variable selection
资金
- National Science Foundation grant [DMS 1555244, MMS 1733572, DMS 1811245]
- National Cancer Institute [P01 CA142538]
- Oak Ridge Associated Universities
We consider integrating a non-probability sample with a probability sample which provides high dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded concave penalties to select important variables and show selection consistency for general samples. In the second step, we focus on a doubly robust estimator of the finite population mean and re-estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first-step selection error and renders the doubly robust estimator root n consistent if either the sampling probability or the outcome model is correctly specified.
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