Iddi and Molenberghs (2012) merged the attractive features of the so-called combined model of Molenberghs {\em et al\/} (2010) and the marginalized model of Heagerty (1999) for hierarchical non-Gaussian data with overdispersion. In this model, the fixed-effect parameters retain their marginal interpretation. Lee et al (2011) also developed an extension of Heagerty (1999) to handle zero-inflation from count data, using the hurdle model. To bring together all of these features, a marginalized, zero-inflated, overdispersed model for correlated count data is proposed. Using two empirical sets of data, it is shown that the proposed model leads to important improvements in model fit
Researchers often encounter data which exhibit an excess number of zeroes than would be expected in ...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, a...
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, a...
Marginalised models are in great demand by many researchers in the life sciences, particularly in cl...
© 2014 SAGE Publications. Count data are most commonly modeled using the Poisson model, or by one of...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Health sciences research often involves analyses of repeated measurement or longitudinal count data ...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
© 2013, © 2013 Taylor & Francis. Many applications in public health, medical and biomedical or oth...
Overdispersion and correlation are two features often encountered when modeling non-Gaussian depende...
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....
Public health research often concerns relationships between exposures and correlated count outcomes....
Researchers often encounter data which exhibit an excess number of zeroes than would be expected in ...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, a...
Count data are collected repeatedly over time in many applications, such as biology, epidemiology, a...
Marginalised models are in great demand by many researchers in the life sciences, particularly in cl...
© 2014 SAGE Publications. Count data are most commonly modeled using the Poisson model, or by one of...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Health sciences research often involves analyses of repeated measurement or longitudinal count data ...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
© 2013, © 2013 Taylor & Francis. Many applications in public health, medical and biomedical or oth...
Overdispersion and correlation are two features often encountered when modeling non-Gaussian depende...
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....
Public health research often concerns relationships between exposures and correlated count outcomes....
Researchers often encounter data which exhibit an excess number of zeroes than would be expected in ...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...
The zero-inflated Poisson (ZIP) regression model is often employed in public health research to exam...