Semiparametric Weighting Estimations of a Zero-Inflated Poisson Regression with Missing in Covariates

M. T. Lukusa, F. K.H. Phoa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We scrutinize the problem of missing covariates in the zero-inflated Poisson regression model. Under the assumption that some covariates for modeling the probability of the zero and the nonzero states are missing at random, the complete-case estimator is known to be biased and inefficient. Although the inverse probability weighting estimator is unbiased, it remains inefficient. We propose four types of semiparametric weighting estimations where the conditional probabilities and the conditional expected score functions are estimated either by using the generalized additive models (GAMs) and the Nadaraya kernel smoother method. In addition, we allow the conditional probabilities and the conditional expectations to be either of the same types or of different types. Moreover, a Monte Carlo experiment is used to investigate the merit of the proposed method.

Original languageEnglish
Title of host publicationNonparametric Statistics - 4th ISNPS 2018
EditorsMichele La Rocca, Brunero Liseo, Luigi Salmaso
PublisherSpringer
Pages329-339
Number of pages11
ISBN (Print)9783030573058
DOIs
Publication statusPublished - 2020
Event4th Conference of the International Society for Nonparametric Statistics, ISNPS 2018 - Salerno, Italy
Duration: 2018 Jun 112018 Jun 15

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume339
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference4th Conference of the International Society for Nonparametric Statistics, ISNPS 2018
CountryItaly
CitySalerno
Period18-06-1118-06-15

All Science Journal Classification (ASJC) codes

  • Mathematics(all)

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