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Asymptotic properties and information criteria for misspecified generalized linear mixed models

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The problem of misspecification poses challenges in model selection. The paper studies the asymptotic properties of estimators for generalized linear mixed models with misspecification under the framework of conditional Kullback–Leibler divergence. A conditional generalized information criterion is introduced, and a model selection procedure is proposed by minimizing the criterion. We prove that the model selection procedure proposed is asymptotically loss efficient when all the candidate models are misspecified. The model selection consistency of the model selection procedure is also established when the true data‐generating procedure lies within the set of candidate models. Simulation experiments confirm the effectiveness of the method proposed. The use of the criterion for model selection is illustrated through an analysis of the European Currency Opinion Survey data.
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Keywords: Asymptotic loss efficiency; Conditional inference; Misspecified generalized linear mixed model; Model selection; Penalized likelihood

Document Type: Research Article

Publication date: September 1, 2018

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