Doubly robust estimation of partially linear models for longitudinal data with dropouts and measurement error in covariates. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Lin, Huiming; Qin, Guoyou; Zhang, Jiajia; Fung, Wing K.
• Is Part Of: Statistics. Volume 52:Issue 1(2018)
• Page Start: 84
• Page End: 98
• Publication Date: 2018-01-02
• Subjects: Doubly robust -- dropouts -- measurement error -- partially linear models; 62-07; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2017.1361957
• Languages: English
• ISSNs: 0233-1888

Abstract

ABSTRACT: In longitudinal studies, missing responses and mismeasured covariates are commonly seen due to the data collection process. Without cautiousness in data analysis, inferences from the standard statistical approaches may lead to wrong conclusions. In order to improve the estimation for longitudinal data analysis, a doubly robust estimation method for partially linear models, which can simultaneously account for the missing responses and mismeasured covariates, is proposed. Imprecisions of covariates are corrected by taking advantage of the independence between replicate measurement errors, and missing responses are handled by the doubly robust estimation under the mechanism of missing at random. The asymptotic properties of the proposed estimators are established under regularity conditions, and simulation studies demonstrate desired properties. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study.