Doubly robust estimation of partially linear models for longitudinal data with dropouts and measurement error in covariates
| Autor: | Jiajia Zhang, Wing K. Fung, Guoyou Qin, Huiming Lin |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Data collection Observational error Linear model Estimator 030209 endocrinology & metabolism Replicate Missing data 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Statistics Covariate Econometrics 0101 mathematics Statistics Probability and Uncertainty Independence (probability theory) Mathematics |
| Zdroj: | Statistics. 52:84-98 |
| ISSN: | 1029-4910 0233-1888 |
| DOI: | 10.1080/02331888.2017.1361957 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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