Paper Info
Title
Approximate conditional likelihood for generalized linear models with general missing data mechanism.
Abstract
The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate. Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.
Year
DOI
Venue
2017
10.1007/s11424-017-6188-3
J. Systems Science & Complexity
Keywords
Field
DocType
Asymptotic normality, generalized linear model, identifiability, missing data mechanism, non-canonical link function, nonignorable missingness
Econometrics,Covariate,Identifiability,Generalized linear model,Missing data,Mathematics,Estimator,Asymptotic distribution
Journal
Volume
Issue
ISSN
30
1
1559-7067
Citations 
PageRank 
References 
2
0.51
0
Authors
2
Name
Order
Citations
PageRank
jiwei zhao131.81
Jun Shao221.86