Kouakou Mathias Amani, Ouagnina Hili, Konan Jean Geoffroy Kouakou
{"title":"Statistical Inference in Marginalized Zero-inflated Poisson Regression Models with Missing Data in Covariates","authors":"Kouakou Mathias Amani, Ouagnina Hili, Konan Jean Geoffroy Kouakou","doi":"10.3103/s1066530723040038","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The marginalized zero-inflated poisson (MZIP) regression model\nquantifies the effects of an explanatory variable in the mixture\npopulation. Also, in practice the variables are usually partially\nobserved. Thus, we first propose to study the maximum likelihood\nestimator when all variables are observed. Then, assuming that the\nprobability of selection is modeled using mixed covariates\n(continuous, discrete and categorical), we propose a\nsemiparametric inverse-probability weighted (SIPW) method for\nestimating the parameters of the MZIP model with covariates\nmissing at random (MAR). The asymptotic properties (consistency,\nasymptotic normality) of the proposed estimators are established\nunder certain regularity conditions. Through numerical studies,\nthe performance of the proposed estimators was evaluated. Then the\nresults of the SIPW are compared to the results obtained by\nsemiparametric inverse-probability weighted kermel-based (SIPWK)\nestimator method. Finally, we apply our methodology to a dataset\non health care demand in the United States.</p>","PeriodicalId":46039,"journal":{"name":"Mathematical Methods of Statistics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Methods of Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s1066530723040038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
The marginalized zero-inflated poisson (MZIP) regression model
quantifies the effects of an explanatory variable in the mixture
population. Also, in practice the variables are usually partially
observed. Thus, we first propose to study the maximum likelihood
estimator when all variables are observed. Then, assuming that the
probability of selection is modeled using mixed covariates
(continuous, discrete and categorical), we propose a
semiparametric inverse-probability weighted (SIPW) method for
estimating the parameters of the MZIP model with covariates
missing at random (MAR). The asymptotic properties (consistency,
asymptotic normality) of the proposed estimators are established
under certain regularity conditions. Through numerical studies,
the performance of the proposed estimators was evaluated. Then the
results of the SIPW are compared to the results obtained by
semiparametric inverse-probability weighted kermel-based (SIPWK)
estimator method. Finally, we apply our methodology to a dataset
on health care demand in the United States.
期刊介绍:
Mathematical Methods of Statistics is an is an international peer reviewed journal dedicated to the mathematical foundations of statistical theory. It primarily publishes research papers with complete proofs and, occasionally, review papers on particular problems of statistics. Papers dealing with applications of statistics are also published if they contain new theoretical developments to the underlying statistical methods. The journal provides an outlet for research in advanced statistical methodology and for studies where such methodology is effectively used or which stimulate its further development.