Heterogeneity, Excess Zeros, and the Structure of Count Data Models

Mullahy, John

doi:10.1002/(sici)1099-1255(199705)12:3<337::aid-jae438>3.0.co;2-g

Cited by 130 publications

(72 citation statements)

References 21 publications

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“…We used logistic regression models to estimate the probability of any use of inpatient and crisis or residential services. We used zero-inflated negative binomial regression models to estimate the number of outpatient visits (24)(25)(26). The primary independent variables of interest were indicator variables for levels of fidelity to the Housing First model and for the interactions between levels of fidelity and the post period.…”

Section: Study Design and Statistical Analysismentioning

confidence: 99%

Fidelity to the Housing First Model and Variation in Health Service Use Within Permanent Supportive Housing

Gilmer¹,

Stefancic²,

Henwood³

et al. 2015

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Section: Study Design and Statistical Analysismentioning

confidence: 99%

Fidelity to the Housing First Model and Variation in Health Service Use Within Permanent Supportive Housing

Gilmer¹,

Stefancic²,

Henwood³

et al. 2015

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“…Detailed explanations of two-part regression models can be found in Mullahy. 53 The person-level regression models controlled for demographics (age, gender), location (residence in a rural vs. urban area), and comorbidities (using the Charlson Comorbidity Index (CCI) and the number of psychiatric problems found in the medical claims data). The predictive power of the regression models was then estimated, by comparing mean predicted expenditures with actual average expenditures for claims incurred in 2005.…”

Section: Assessing the Validity And Reliability Of The Eatmentioning

confidence: 99%

Development of the Environmental Assessment Tool (EAT) to Measure Organizational Physical and Social Support for Worksite Obesity Prevention Programs

DeJoy

Wilson

Goetzel

et al. 2008

Journal of Occupational &Amp; Environmental Medicine

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Objective-To describe the development, reliability, and validity of the Environmental Assessment Tool (EAT) for assessing worksite physical and social environmental support for obesity prevention.Methods-The EAT was developed using a multi-step process. Inter-rater reliability was estimated via Kappa and other measures. Concurrent and predictive validity were estimated using site-level correlations and person-level multiple regression analyses comparing EAT scores and employee absenteeism and healthcare expenditures. Results-Resultsshow high inter-rater reliability and concurrent validity for many measures and predictive validity for absenteeism expenditures.Conclusions-The primary use of the EAT is as a physical and social environment assessment tool for worksite obesity prevention efforts. It can be used as a reliable and valid means to estimate relationships between environmental interventions and absenteeism and medical expenditures, provided those expenditures are for the same year that the EAT is administered.

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“…Because of its single parameter (having no dispersion parameter), many scenarios were necessary to construct suitable count distributions using some indexes as measures to detect departures from Poisson distribution. For instance, the most known, so frequent and well-explained of phenomena are overdispersion and zero-in ‡ation (Hall and Berenhaut 2002;Mullahy 1997). Of course the opposite phenomena exist but is uncommon (Bosch and Ryan 1998;Castillo and Pérez-Casany 2005;Kokonendji and Mizère 2005).…”

Section: Introductionmentioning

confidence: 99%

On Hinde–Demétrio regression models for overdispersed count data

Kokonendji

Demétrio

Zocchi

2007

Statistical Methodology

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In this paper we introduce the Hinde-Demétrio (HD) regression models for analyzing overdispersed count data and, mainly, investigate the e¤ect of dispersion parameter. The HD distributions are discrete additive exponential dispersion models (depending on canonical and dispersion parameters) with a third real index parameter p and have been characterized by its unit variance function + p . For p equals to 2; 3; , the corresponding distributions are concentrated on nonnegative integers, overdispersed and zero-in ‡ated with respect to a Poisson distribution having the same mean. The negative binomial (p = 2), strict arcsine (p = 3) and Poisson (p ! 1) distributions are particular count HD families. From generalized linear modelling framework, the e¤ect of dispersion parameter in the HD regression models, among other things, is pointed out through the double mean parametrization: unit and standard means. In the particular additive model, this e¤ect must be negligible within an adequate HD model for …xed integer p. The estimation of the integer p is also examined separately. The results are illustrated and discussed on a horticultural data set.

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Heterogeneity, Excess Zeros, and the Structure of Count Data Models

Cited by 130 publications

References 21 publications

Fidelity to the Housing First Model and Variation in Health Service Use Within Permanent Supportive Housing

Fidelity to the Housing First Model and Variation in Health Service Use Within Permanent Supportive Housing

Development of the Environmental Assessment Tool (EAT) to Measure Organizational Physical and Social Support for Worksite Obesity Prevention Programs

On Hinde–Demétrio regression models for overdispersed count data

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