Locally efficient semiparametric estimator for zero-inflated Poisson model with error-prone covariates. Issue 6 (13th April 2021)

• Record Type: Journal Article
• Authors: Liu, Jianxuan; Eftekharnejad, Sara
• Is Part Of: Journal of statistical computation and simulation. Volume 91:Issue 6(2021)
• Page Start: 1092
• Page End: 1107
• Publication Date: 2021-04-13
• Subjects: Error-prone covariates -- local efficiency -- zero-inflated Poisson model -- regression calibration -- semiparametric methods; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1840569
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Overdispersion is a common phenomenon in count or frequency responses in Poisson models. For example, number of car accidents on a highway during a year period. A similar phenomenon is observed in electric power systems, where cascading failures often follows some distribution with inflated zero. When the response contains an excess amount of zeros, zero-inflated Poisson (ZIP) is the most favourable model. However, during the data collection process, some of the covariates cannot be accessed directly or are measured with error among numerous disciplines. To the best of our knowledge, little existing work is available in the literature that tackles the population heterogeneity in the count response while some of the covariates are measured with error. With the increasing popularity of such outcomes in modern studies, it is interesting and timely to study zero-inflated Poisson models in which some of the covariates are subject to measurement error while some are not. We propose a flexible partial linear single index model for the log Poisson mean to correct bias potentially due to the error in covariates or the population heterogeneity. We derive consistent and locally efficient semiparametric estimators and study the large sample properties. We further assess the finite sample performance through simulation studies. Finally, we apply the proposed method to a real data application and compare with existing methods that handle measurement error in covariates.