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J. Robins, R. Gill (1997)
Non-response models for the analysis of non-monotone ignorable missing data.Statistics in medicine, 16 1-3
(2005)
Testing lack-of-fit for general linear errors in variables models
S. Lipsitz, J. Ibrahim, L. Zhao (1999)
A Weighted Estimating Equation for Missing Covariate Data with Properties Similar to Maximum LikelihoodJournal of the American Statistical Association, 94
Lueping Zhao, Stuart Lipsitz, Danika Lew (1996)
Regression analysis with missing covariate data using estimating equations.Biometrics, 52 4
N. Lazar (2003)
Statistical Analysis With Missing DataTechnometrics, 45
P. Diggle, M. Kenward (1994)
Informative Drop‐Out in Longitudinal Data AnalysisApplied statistics, 43
Rotnitzky Andrea, D. Scharfstein, Ting-Li Su, J. Robins (2001)
Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of CensoringBiometrics, 57
Stuart Lipsitz, Joseph Ibrahim, Ming-Hui Chen, Harriet Peterson (1999)
Non-ignorable missing covariates in generalized linear models.Statistics in medicine, 18 17-18
J. Lamperti (1962)
ON CONVERGENCE OF STOCHASTIC PROCESSESTransactions of the American Mathematical Society, 104
Qingxia Chen, J. Ibrahim (2006)
Semiparametric Models for Missing Covariate and Response Data in Regression ModelsBiometrics, 62
(1993)
Testing parametric versus nonparametric regression
W. Stute, W. Manteiga, M. Quindimil (1998)
Bootstrap Approximations in Model Checks for RegressionJournal of the American Statistical Association, 93
A.W. Vaart, J.A. Wellner (1996)
Weak Convergence and Empirical Processes
In this paper, we investigate the model checking problem for a general linear model with nonignorable missing covariates. We show that, without any parametric model assumption for the response probability, the least squares method yields consistent estimators for the linear model even if only the complete data are applied. This makes it feasible to propose two testing procedures for the corresponding model checking problem: a score type lack-of-fit test and a test based on the empirical process. The asymptotic properties of the test statistics are investigated. Both tests are shown to have asymptotic power 1 for local alternatives converging to the null at the rate n −r , 0 ≤ r < 1/2. Simulation results show that both tests perform satisfactorily.
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Dec 13, 2011
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