Frequentist model averaging for zero-inflated Poisson regression models

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)679-691
Journal / PublicationStatistical Analysis and Data Mining
Issue number6
Online published5 Oct 2022
Publication statusPublished - Dec 2022


This paper considers frequentist model averaging for estimating the unknown parameters of the zero-inflated Poisson regression model. Our proposed weight choice procedure is based on the minimization of an unbiased estimator of a conditional quadratic loss function. We prove that the resulting model average estimator enjoys optimal asymptotic property and improves finite sample properties over the two commonly used information-based model selection estimators and their model average estimators via simulation studies. The proposed method is illustrated by a real data example.

Research Area(s)

  • count data, loss function, model averaging, stacking, zero-inflated Poisson regression model

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