Instrumental variable estimation in generalized linear mixed models with measurement error

报告题目:Instrumental variable estimation in generalized linear mixed models with measurement error

报告人:Liqun Wang, Department of Statistics, University of Manitoba, Canada



摘要:We propose the instrumental variable method for consistent estimation of generalized linear mixed models with measurement error. This method does not require parametric assumptions for the distributions of the unobserved covariates or the measurement errors, and it allows random effects to have any parametric distributions (not necessarily normal). We also propose simulation-based estimators for the situation where the marginal moments do not have closed forms. The proposed estimators are not only computationally attractive but also strongly root-n consistent under general regularity conditions. Moreover, the proposed estimators are robust against data outliers. The methodology is illustrated through examples and simulation studies.

报告人简介:A short bio of Liqun Wang obtained his Ph.D. degree in Statistics and Econometrics at the Vienna University of Technology, Austria. He was a Research Fellow at the Universities of Hannover and Dortmund, Germany, and an Assistant Professor at the University of Basel, Switzerland. Currently he is a Professor in the Department of Statistics of the University of Manitoba, Canada. Liqun Wang is an Elected Member of the International Statistical Institute (ISI). He is an Editor-in-Chief of the Springer journal "Statistical Papers". Liqun Wang’s research interests include nonlinear semiparametric inference, measurement error problems in regression analysis, boundary crossing probability of stochastic processes, and statistical computation.




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