Campus Units
Statistics
Document Type
Article
Publication Version
Published Version
Publication Date
1-2017
Journal or Book Title
Statistica Sinica
Volume
27
Issue
1
First Page
261
Last Page
285
DOI
10.5705/ss.2014.174
Abstract
Parameter estimation in parametric regression models with missing covariates is considered under a survey sampling setup. Under missingness at random, a semiparametric maximum likelihood approach is proposed which requires no parametric specification of the marginal covariate distribution. By drawing from the von Mises calculus and V-Statistics theory, we obtain an asymptotic linear representation of the semiparametric maximum likelihood estimator (SMLE) of the regression parameters, which allows for a consistent estimator of asymptotic variance. An EM algorithm for computation is then developed to implement the proposed method using fractional imputation. Simulation results suggest that the SMLE method is robust, whereas the fully parametric method is subject to severe bias under model misspecification. A rangeland study from the National Resources Inventory (NRI) is used to illustrate the practical use of the proposed methodology.
Copyright Owner
Academia Sinica
Copyright Date
2017
Language
en
File Format
application/pdf
Recommended Citation
Yang, Shu and Kim, Jae Kwang, "A semiparametric inference to regression analysis with missing covariates in survey data" (2017). Statistics Publications. 119.
https://lib.dr.iastate.edu/stat_las_pubs/119
Included in
Design of Experiments and Sample Surveys Commons, Multivariate Analysis Commons, Statistical Methodology Commons
Comments
This article is published as Yang, S. and J.K. Kim (2017). “A semiparametric inference to regression analysis with missing covariates in survey data”, Statistica Sinica, 27, 261-285. doi:10.5705/ss.2014.174. Posted with permission.