Marginalised models are in great demand by many researchers in the life sciences, particularly in clinical trials, epidemiology, health-economics, surveys and many others, since they allow generalisation of inference to the entire population under study. For count data, standard procedures such as the Poisson regression and negative binomial model provide population average inference for model parameters. However, occurrence of excess zero counts and lack of independence in empirical data have necessitated their extension to accommodate these phenomena. These extensions, though useful, complicate interpretations of effects. For example, the zero-inflated Poisson model accounts for the presence of excess zeros, but the parameter estimates do...
Count data with excessive zeros and/or over-dispersion are prevalent in a wide variety of discipline...
We consider the problem of modelling count data with excess zeros and over-dispersion which are comm...
The zero inflated models are usually used in modeling count data with excess zeros where the existen...
Health sciences research often involves analyses of repeated measurement or longitudinal count data ...
Violations of Poisson assumptions usually result in overdispersion, where the variance of the model ...
Iddi and Molenberghs (2012) merged the attractive features of the so-called combined model of Molenb...
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, a...
A natural approach to analyzing the effect of covariates on a count response variable is to use a P...
A Poisson regression model is commonly used to model count data. The Poisson model assumes equidispe...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
Applications of zero-inflated count data models have proliferated in health economics. However, zero...
The performance of several models under different conditions of zero-inflation and dispersion are ev...
Public health research often concerns relationships between exposures and correlated count outcomes....
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
Examples of zero-inflated Poisson and negative binomial regression models were used to demonstrate c...
Count data with excessive zeros and/or over-dispersion are prevalent in a wide variety of discipline...
We consider the problem of modelling count data with excess zeros and over-dispersion which are comm...
The zero inflated models are usually used in modeling count data with excess zeros where the existen...
Health sciences research often involves analyses of repeated measurement or longitudinal count data ...
Violations of Poisson assumptions usually result in overdispersion, where the variance of the model ...
Iddi and Molenberghs (2012) merged the attractive features of the so-called combined model of Molenb...
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, a...
A natural approach to analyzing the effect of covariates on a count response variable is to use a P...
A Poisson regression model is commonly used to model count data. The Poisson model assumes equidispe...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
Applications of zero-inflated count data models have proliferated in health economics. However, zero...
The performance of several models under different conditions of zero-inflation and dispersion are ev...
Public health research often concerns relationships between exposures and correlated count outcomes....
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
Examples of zero-inflated Poisson and negative binomial regression models were used to demonstrate c...
Count data with excessive zeros and/or over-dispersion are prevalent in a wide variety of discipline...
We consider the problem of modelling count data with excess zeros and over-dispersion which are comm...
The zero inflated models are usually used in modeling count data with excess zeros where the existen...