Journal article
Generalized partially linear models with missing covariates
Journal of multivariate analysis, v 99(5), pp 880-895
2008
PMID: 19412320
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Abstract
In this article we study a semiparametric generalized partially linear model when the covariates are missing at random. We propose combining local linear regression with the local quasilikelihood technique and weighted estimating equation to estimate the parameters and nonparameters when the missing probability is known or unknown. We establish normality of the estimators of the parameter and asymptotic expansion for the estimators of the nonparametric part. We apply the proposed models and methods to a study of the relation between virologic and immunologic responses in AIDS clinical trials, in which virologic response is classified into binary variables. We also give simulation results to illustrate our approach.
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Details
- Title
- Generalized partially linear models with missing covariates
- Creators
- Hua Liang - University of Rochester Medical Center
- Publication Details
- Journal of multivariate analysis, v 99(5), pp 880-895
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000255291900006
- Scopus ID
- 2-s2.0-40749158174
- Other Identifier
- 991019320612504721
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- Web of Science research areas
- Statistics & Probability