Untangle the structural and random zeros in statistical modelings

dc.contributor.authorTang, W.
dc.contributor.authorHe, H.
dc.contributor.authorWang, W.J.
dc.contributor.authorChen, Ding-Geng (Din)
dc.date.accessioned2018-11-12T09:48:55Z
dc.date.issued2018
dc.description.abstractCount data with structural zeros are common in public health applications. There are considerable researches focusing on zero-inflated models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) models for such zero-inflated count data when used as response variable. However, when such variables are used as predictors, the difference between structural and random zeros is often ignored and may result in biased estimates. One remedy is to include an indicator of the structural zero in the model as a predictor if observed. However, structural zeros are often not observed in practice, in which case no statistical method is available to address the bias issue. This paper is aimed to fill this methodological gap by developing parametric methods to model zero-inflated count data when used as predictors based on the maximum likelihood approach. The response variable can be any type of data including continuous, binary, count or even zero-inflated count responses. Simulation studies are performed to assess the numerical performance of this new approach when sample size is small to moderate. A real data example is also used to demonstrate the application of this method.en_ZA
dc.description.departmentStatisticsen_ZA
dc.description.embargo2018-11-24
dc.description.librarianhj2018en_ZA
dc.description.sponsorshipThe National Institute on Drug Abuse R33DA027521, National Institute of General Medical Sciences R01GM108337, and National Institute of Child Health and Human Development R01HD075635.en_ZA
dc.description.urihttp://www.tandfonline.com/loi/cjas20en_ZA
dc.identifier.citationW. Tang, H. He, W.J. Wang & D.G. Chen (2018) Untangle the structural and random zeros in statistical modelings, Journal of Applied Statistics, 45:9, 1714-1733, DOI: 10.1080/02664763.2017.1391180.en_ZA
dc.identifier.issn0266-4763 (print)
dc.identifier.issn1360-0532 (online)
dc.identifier.other10.1080/02664763.2017.1391180
dc.identifier.urihttp://hdl.handle.net/2263/67189
dc.language.isoenen_ZA
dc.publisherTaylor and Francisen_ZA
dc.rights© 2017 Informa UK Limited, trading as Taylor & Francis Group. This is an electronic version of an article published in Journal of Applied Statistics, vol. 45, no. 9, pp. 1714-1733, 2018. doi : 10.1080/02664763.2017.1391180. Journal of Applied Statistics is available online at : http://www.tandfonline.comloi/cjas20.en_ZA
dc.subjectZero-inflated poisson (ZIP)en_ZA
dc.subjectZero-inflated negative binomial (ZINB)en_ZA
dc.subjectGeneralized linear modelsen_ZA
dc.subjectMaximum likelihooden_ZA
dc.subjectStructural zerosen_ZA
dc.subjectZero-inflated explanatory variablesen_ZA
dc.titleUntangle the structural and random zeros in statistical modelingsen_ZA
dc.typePostprint Articleen_ZA

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