A pairwise likelihood approach for longitudinal data with missing observations in both response and covariates

[1]  J. Ashford,et al.  Multi-variate probit analysis. , 1970, Biometrics.

[2]  W. Newey,et al.  Large sample estimation and hypothesis testing , 1986 .

[3]  D. Rubin.  Multiple imputation for nonresponse in surveys , 1989 .

[4]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[5]  Roderick J. A. Little.  Regression with Missing X's: A Review , 1992 .

[6]  M. Kenward,et al.  Informative Drop‐Out in Longitudinal Data Analysis , 1994 .

[7]  J. Robins,et al.  Analysis of semiparametric regression models for repeated outcomes in the presence of missing data , 1995 .

[8]  Roderick J. A. Little,et al.  Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .

[9]  Nan M. Laird,et al.  Multivariate Logistic Models for Incomplete Binary Responses , 1996 .

[10]  A. Azzalini,et al.  The multivariate skew-normal distribution , 1996 .

[11]  Stuart R. Lipsitz,et al.  Marginal models for the analysis of longitudinal measurements with nonignorable non-monotone missing data , 1998 .

[12]  H. Joe.  Multivariate models and dependence concepts , 1998 .

[13]  James M. Robins,et al.  Semiparametric Regression for Repeated Outcomes With Nonignorable Nonresponse , 1998 .

[14]  K. Brown,et al.  Effectiveness of a social influences smoking prevention program as a function of provider type, training method, and school risk. , 1999, American journal of public health.

[15]  N M Laird,et al.  Maximum likelihood analysis of generalized linear models with missing covariates , 1999, Statistical methods in medical research.

[16]  M. H. Chen,et al.  Non-ignorable missing covariates in generalized linear models. , 1999, Statistics in medicine.

[17]  Joseph G. Ibrahim,et al.  Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable , 1999 .

[18]  S. Lipsitz,et al.  Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable , 2001 .

[19]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

[20]  Joseph G Ibrahim,et al.  Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models , 2003, Biometrics.

[21]  Geert Molenberghs,et al.  A pairwise likelihood approach to estimation in multilevel probit models , 2004, Comput. Stat. Data Anal..

[22]  H. Y. Chen.  Nonparametric and Semiparametric Models for Missing Covariates in Parametric Regression , 2004 .

[23]  D. Cox,et al.  A note on pseudolikelihood constructed from marginal densities , 2004 .

[24]  N. Rao Chaganty,et al.  Efficiency of generalized estimating equations for binary responses , 2004 .

[25]  S. Eguchi,et al.  A paradox concerning nuisance parameters and projected estimating functions , 2004 .

[26]  S. Lipsitz,et al.  Missing-Data Methods for Generalized Linear Models , 2005 .

[27]  G. Molenberghs,et al.  Models for Discrete Longitudinal Data , 2005 .

[28]  LIKELIHOOD-BASED INFERENCE WITH NONIGNORABLE MISSING RESPONSES AND COVARIATES IN MODELS FOR DISCRETE LONGITUDINAL DATA , 2006 .

[29]  Joseph G Ibrahim,et al.  Pseudo‐likelihood methods for longitudinal binary data with non‐ignorable missing responses and covariates , 2006, Statistics in medicine.

[30]  Wei Liu,et al.  Simultaneous Inference for Semiparametric Nonlinear Mixed‐Effects Models with Covariate Measurement Errors and Missing Responses , 2007, Biometrics.

[31]  Michelle Shardell,et al.  Weighted estimating equations for longitudinal studies with death and non‐monotone missing time‐dependent covariates and outcomes , 2008, Statistics in medicine.

[32]  Joseph G Ibrahim,et al.  Theory and Inference for Regression Models with Missing Responses and Covariates. , 2008, Journal of multivariate analysis.

[33]  Richard J. Cook,et al.  Weighted Generalized Estimating Functions for Longitudinal Response and Covariate Data That Are Missing at Random , 2010 .

[34]  Geert Molenberghs,et al.  A weighted combination of pseudo-likelihood estimators for longitudinal binary data subject to non-ignorable non-monotone missingness. , 2010, Statistics in medicine.

[35]  Richard J. Cook,et al.  A robust pairwise likelihood method for incomplete longitudinal binary data arising in clusters , 2011 .

[36]  G. Yi,et al.  A Pairwise Likelihood Method For Correlated Binary Data With/withoutMissing Observations Under Generalized Partially Linear Single-indexModels , 2011 .

[37]  Lang Wu,et al.  Simultaneous inference and bias analysis for longitudinal data with covariate measurement error and missing responses. , 2011, Biometrics.

[38]  Bruce G. Lindsay,et al.  ISSUES AND STRATEGIES IN THE SELECTION OF COMPOSITE LIKELIHOODS , 2011 .

[39]  Maengseok Noh,et al.  Hierarchical likelihood methods for nonlinear and generalized linear mixed models with missing data and measurement errors in covariates , 2012, J. Multivar. Anal..

[40]  Harry Joe,et al.  Composite Likelihood Methods , 2012 .

[41]  Geert Molenberghs,et al.  A nonparametric approach to weighted estimating equations for regression analysis with missing covariates , 2012, Comput. Stat. Data Anal..

[42]  Xiaohui Liu,et al.  Estimation in Partially Linear Single-Index Models with Missing Covariates , 2012 .

[43]  Michael Parzen.  Pseudo-likelihood Methods for the Analysis of Longitudinal Binary Data Subject to Nonignorable Non-monotone Missingness , 2021, Journal of Data Science.