A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

Gul Inan; John Preisser; Kalyan Das

doi:10.1007/s13253-017-0314-5

A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

Gul Inan^*
, John Preisser
, Kalyan Das

^*Bu çalışma için yazışmadan sorumlu yazar

Middle East Technical University
University of North Carolina at Chapel Hill
University of Calcutta

Araştırma sonucu: Dergiye katkı › Makale › bilirkişi

6 Atıf (Scopus)

Özet

Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	113-128
Sayfa sayısı	16
Dergi	Journal of Agricultural, Biological, and Environmental Statistics
Hacim	23
Basın numarası	1
DOI'lar	https://doi.org/10.1007/s13253-017-0314-5
Yayın durumu	Yayınlandı - 1 Mar 2018
Harici olarak yayınlandı	Evet

Bibliyografik not

Publisher Copyright:
© 2017, International Biometric Society.

Finansman

Part of this study was carried out, while Gul Inan was visiting Department of Biostatistics, University of North Carolina—Chapel Hill, USA. She would like to thank the Scientific and Technological Research Council of Turkey (TUBITAK) for funding her postdoctoral studies in USA. This study was supported by the International Biometric Society and Institute of Mathematical Statistics travel grant programmes to be presented at 9th Conference of the Eastern Mediterranean Region of the International Biometric Society and Joint Statistical Meetings, respectively, in 2017.

Finansörler
Eastern Mediterranean Region of the International Biometric Society
Institute of Mathematical Statistics
TUBITAK
International Biometric Society
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Belgeye Erişim

10.1007/s13253-017-0314-5

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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doi = "10.1007/s13253-017-0314-5",

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AU - Preisser, John

AU - Das, Kalyan

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.

AB - Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.

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KW - Excess zeros

KW - Marginal models

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KW - Score test

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AN - SCOPUS:85034646033

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ER -

A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

Özet

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap