Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data

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dc.contributor.author Yang, Shu
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Song, Rui
dc.contributor.department Statistics
dc.date 2019-12-17T20:11:39.000
dc.date.accessioned 2020-07-02T06:57:32Z
dc.date.available 2020-07-02T06:57:32Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2020-04-01
dc.description.abstract <p>Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. 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 for the sampling score of selection into the non-probability sample and the outcome model. We show that the penalized estimating equation approach enjoys the selection consistency property for general probability samples. The major technical hurdle is due to the possible dependence of the sample under the finite population framework. To overcome this challenge, we construct martingales which enable us to apply Bernstein concentration inequality for martingales. 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.</p>
dc.description.comments <p>This is a manuscript of an article published as Yang, Shu, Jae Kwang Kim, and Rui Song. "Doubly robust inference when combining probability and non‐probability samples with high dimensional data." <em>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</em> 82 (2020): 445-465. doi: <a href="https://doi.org/10.1111/rssb.12354">10.1111/rssb.12354</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/264/
dc.identifier.articleid 1270
dc.identifier.contextkey 15169714
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/264
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90581
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/264/2019_Kim_DoublyRobustPreprint.pdf|||Fri Jan 14 23:03:03 UTC 2022
dc.source.uri 10.1111/rssb.12354
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Probability
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Data integration
dc.subject.keywords Double robustness
dc.subject.keywords Generalizability
dc.subject.keywords Penalized estimating equation
dc.subject.keywords Variable selection
dc.title Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication fdf914ae-e48d-4f4e-bfa2-df7a755320f4
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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