Bivariate Poisson–Poisson model of zero-inflated absenteeism data

Cheung, Yin Bun; Lam, Kwok Fai

doi:10.1002/sim.2485

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Moreover, clustered or general multivariate zero-inflated count data arise naturally in many areas of research like ecology, biodiversity, public health, and medicine. Interests in the analysis of clustered or multivariate zero-inflated count data [16,25,26] or even repeated measures zero-inflated count data [27,28] are growing over the past decade. An interesting example is the bladder cancer study conducted by the Veterans Administration Co-operative Urological Research Group [29].…”

Section: Discussionmentioning

confidence: 99%

Modeling zero‐inflated count data using a covariate‐dependent random effect model

Wong

Lam

2012

Statistics in Medicine

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In various medical related researches, excessive zeros, which make the standard Poisson regression model inadequate, often exist in count data. We proposed a covariate-dependent random effect model to accommodate the excess zeros and the heterogeneity in the population simultaneously. This work is motivated by a data set from a survey on the dental health status of Hong Kong preschool children where the response variable is the number of decayed, missing, or filled teeth. The random effect has a sound biological interpretation as the overall oral health status or other personal qualities of an individual child that is unobserved and unable to be quantified easily. The overall measure of oral health status, responsible for accommodating the excessive zeros and also the heterogeneity among the children, is covariate dependent. This covariate-dependent random effect model allows one to distinguish whether a potential covariate has an effect on the conceived overall oral health condition of the children, that is, the random effect, or has a direct effect on the magnitude of the counts, or both. We proposed a multiple imputation approach for estimation of the parameters. We discussed the choice of the imputation size. We evaluated the performance of the proposed estimation method through simulation studies, and we applied the model and method to the dental data.

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Section: Discussionmentioning

confidence: 99%

Modeling zero‐inflated count data using a covariate‐dependent random effect model

Wong

Lam

2012

Statistics in Medicine

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“…Note that the variance-covariance matrix of the regression parameters can be calculated by taking the inverse of the information matrix in (7). This estimation techniques presented in this section are used in next section to analyze the traffic accidents data [25][26][27][28][29][30].…”

Section: Parameter Estimationmentioning

confidence: 99%

"Modelling Zero-inflated Bivariate Count Responses using Marginal-Conditional Approach with application to Traffic Accidents and Fatalities Data"

M¹

2019

EAES

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Zero-inflated bivariate count responses frequently occur in medical, environmental, ecological, social and transportation studies. These bivariate count responses recorded from a group of independent subjects or from a specific time or place may suffer the presence of excessive zeros, so constitute complexity while modelling. Although the responses under the umbrella of zero-inflated clustered and longitudinal count setup have been studied extensively in the past two decades, the literature is rather limited for analyzing zero-heavy bivariate count data. In this paper, we propose a marginal-conditional approach based bivariate zero-inflated Poisson-Poisson regression model to overcome the complexity imposed due to excessive zeros in bivariate count responses. The estimation and test procedures are developed using the likelihood function evolved from the marginal and conditional approaches. The proposed approach will be illustrated with the application to two traffic accidents and fatalities data collected from Virginia, USA and Groningen, The Netherlands.

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Zero Inflated Bivariate Poisson Regression Models for a Sport (in)activity Data Analysis

Iannario

Ntzoufras

Tarantola

2023

Springer Proceedings in Mathematics &Amp; Statistics

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Bivariate Poisson–Poisson model of zero-inflated absenteeism data

Cited by 7 publications

References 27 publications

Modeling zero‐inflated count data using a covariate‐dependent random effect model

Modeling zero‐inflated count data using a covariate‐dependent random effect model

"Modelling Zero-inflated Bivariate Count Responses using Marginal-Conditional Approach with application to Traffic Accidents and Fatalities Data"

Zero Inflated Bivariate Poisson Regression Models for a Sport (in)activity Data Analysis

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