Estimation in partially linear models with missing covariates Academic Article

abstract

• The partially linear model Y = X T + v(Z) + 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 depending on (Y, Z). New methods are developed for estimating and v() 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.

authors

• Carroll, Raymond

published proceedings

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

author list (cited authors)

• Liang, H., Wang, S. J., Robins, J. M., & Carroll, R. J.

citation count

• 96

complete list of authors

• Liang, H||Wang, SJ||Robins, JM||Carroll, RJ

publication date

• June 2004

publisher

• Taylor & Francis  Publisher

published in

• JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION  Journal

keywords

• Bootstrap
• Clustered Data
• Efficient Score
• Estimating Equation
• Local Linear Regression
• Longitudinal Data
• Missing Co-variates
• Nonparametric Regression
• Semiparametric Estimation

Digital Object Identifier (DOI)

• 10.1198/016214504000000421

start page

• 357

end page

• 367

volume

• 99

issue

• 466

URL

• http%3A%2F%2Fdx.doi.org%2F10.1198%2F016214504000000421