ArticleJournal of survey statistics and methodology2022
Nonparametric Mass Imputation for Data Integration.
Article in Journal of survey statistics and methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2024Article
- Article
- Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration.BMC public health · 2023Article
- Diagnostic and Medication Treatment Disparities in African American Children with ADHD: a Literature Review.Journal of racial and ethnic health disparities · 2022Review
- Universal adaptability: Target-independent inference that competes with propensity scoring.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Bayesian estimation methods for survey data with potential applications to health disparities research.Wiley interdisciplinary reviewsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Data integration combining a probability sample with another nonprobability sample is an emerging area of research in survey sampling. We consider the case when the study variable of interest is measured only in the nonprobability sample, but comparable auxiliary information is available for both data sources. We consider mass imputation for the probability sample using the nonprobability data as the training set for imputation. The parametric mass imputation is sensitive to parametric model assumptions. To develop improved and robust methods, we consider nonparametric mass imputation for data integration. In particular, we consider kernel smoothing for a low-dimensional covariate and generalized additive models for a relatively high-dimensional covariate for imputation. Asymptotic theories and variance estimation are developed. Simulation studies and real applications show the benefits of our proposed methods over parametric counterparts.
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Registered trials
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