Advanced search
Publication Cover
Journal of the American Statistical Association Volume 99, 2004 - Issue 466
Submit an article Journal homepage
308
Views
93
CrossRef citations to date
0
Altmetric
Primary Article

Estimation in Partially Linear Models With Missing Covariates

Hua Liang Hua Liang is Assistant Member, St. Jude Children's Research Hospital, Memphis, TN 38105 . Suojin Wang is Professor and Raymond J. Carroll is Distinguished Professor, Department of Statistics, Texas A&M University, College Station, TX 77843. James M. Robins is Professor of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA 02115. The authors thank the editor, an associate editor, and two referees for their great patience, constructive comments, and useful suggestions. Liang's research was supported in part by the National Institute of Allergy and Infectious Diseases (U01 AI38855) and the American Lebanese Syrian Associated Charities. Wang and Carroll's research was supported by a grant from the National Cancer Institute (CA-57030) and by the Texas A&M Center for Environmental and Rural Health via a grant from the National Institute of Environmental Health Sciences (P30-ES09106). Robins' research was supported by the National Institutes of Health (AI-32475).
,
Suojin Wang Hua Liang is Assistant Member, St. Jude Children's Research Hospital, Memphis, TN 38105 . Suojin Wang is Professor and Raymond J. Carroll is Distinguished Professor, Department of Statistics, Texas A&M University, College Station, TX 77843. James M. Robins is Professor of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA 02115. The authors thank the editor, an associate editor, and two referees for their great patience, constructive comments, and useful suggestions. Liang's research was supported in part by the National Institute of Allergy and Infectious Diseases (U01 AI38855) and the American Lebanese Syrian Associated Charities. Wang and Carroll's research was supported by a grant from the National Cancer Institute (CA-57030) and by the Texas A&M Center for Environmental and Rural Health via a grant from the National Institute of Environmental Health Sciences (P30-ES09106). Robins' research was supported by the National Institutes of Health (AI-32475).
,
James M Robins Hua Liang is Assistant Member, St. Jude Children's Research Hospital, Memphis, TN 38105 . Suojin Wang is Professor and Raymond J. Carroll is Distinguished Professor, Department of Statistics, Texas A&M University, College Station, TX 77843. James M. Robins is Professor of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA 02115. The authors thank the editor, an associate editor, and two referees for their great patience, constructive comments, and useful suggestions. Liang's research was supported in part by the National Institute of Allergy and Infectious Diseases (U01 AI38855) and the American Lebanese Syrian Associated Charities. Wang and Carroll's research was supported by a grant from the National Cancer Institute (CA-57030) and by the Texas A&M Center for Environmental and Rural Health via a grant from the National Institute of Environmental Health Sciences (P30-ES09106). Robins' research was supported by the National Institutes of Health (AI-32475).
&
Raymond J Carroll Hua Liang is Assistant Member, St. Jude Children's Research Hospital, Memphis, TN 38105 . Suojin Wang is Professor and Raymond J. Carroll is Distinguished Professor, Department of Statistics, Texas A&M University, College Station, TX 77843. James M. Robins is Professor of Epidemiology and Biostatistics, Harvard School of Public Health, Boston, MA 02115. The authors thank the editor, an associate editor, and two referees for their great patience, constructive comments, and useful suggestions. Liang's research was supported in part by the National Institute of Allergy and Infectious Diseases (U01 AI38855) and the American Lebanese Syrian Associated Charities. Wang and Carroll's research was supported by a grant from the National Cancer Institute (CA-57030) and by the Texas A&M Center for Environmental and Rural Health via a grant from the National Institute of Environmental Health Sciences (P30-ES09106). Robins' research was supported by the National Institutes of Health (AI-32475).
Pages 357-367 | Published online: 31 Dec 2011
 
Sample our Mathematics & Statistics journals, sign in here to start your FREE access for 14 days

Abstract

The partially linear model Y = XTβ + ν(Z) + ϵ has been studied extensively when data are completely observed. In this article, we consider the case where the covariate X is sometimes missing, with missingness probability π depending on (Y, Z). New methods are developed for estimating β and ν(·). Our methods are shown to outperform asymptotically methods based only on the complete data. Asymptotic efficiency is discussed, and the semiparametric efficient score function is derived. Justification of the use of the nonparametric bootstrap in this context is sketched. The proposed estimators are extended to a working independence analysis of longitudinal/clustered data and applied to analyze an AIDS clinical trial dataset. The results of a simulation experiment are also given to illustrate our approach.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related Research Data

Semi-parametric regression when some (expensive) covariates are missing by design
Source: Springer Science and Business Media LLC
Robust estimation and inference for general varying coefficient models with missing observations
Source: Springer Science and Business Media LLC
Estimation of Conditional Prevalence From Group Testing Data With Missing Covariates
Source: Informa UK Limited
Two stage smoothing in additive models with missing covariates
Source: Springer Science and Business Media LLC
Statistical estimation in partial linear models with covariate data missing at random
Source: Springer Science and Business Media LLC
Bayesian inference on partially linear mixed-effects joint models for longitudinal data with multiple features
Source: Springer Science and Business Media LLC
Penalized empirical likelihood for quantile regression with missing covariates and auxiliary information
Source: Informa UK Limited
Two-step estimators in partial linear models with missing response variables and error-prone covariates
Source: Springer Science and Business Media LLC
Doubly robust‐type estimation of population moments and parameters in biased sampling
Source: Wiley
GMM estimation in partial linear models with endogenous covariates causing an over-identified problem
Source: Informa UK Limited
estimation with censored regressors: basic issues
Source: Wiley
Semiparametric marginal and association regression methods for clustered binary data
Source: Springer Science and Business Media LLC
Robust estimation of the generalised partial linear model with missing covariates
Source: Informa UK Limited

Linking provided by 

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.