We present motivation and new Stata commands for modeling count data. While the focus of this article is on modeling data with underdispersion, the new command for fitting generalized Poisson regression models is also suitable as an alternative to negative binomial regression for overdispersed data
It is common for time series of unbounded counts (that is, nonnegative integers) to display overdisp...
We present motivation and new commands for modeling heaped count data. These data may appear when su...
We frequently encounter outcomes of count that have extra variation. This paper considers several al...
This article describes the R package CountsEPPM and its use in determining maximum likelihood estima...
Abstract: This paper represents the comparison between Negative Binomial Regression model and Genera...
We present a novel distribution for modelling count data that are underdispersed relative to the Poi...
We present motivation and new commands for modeling count data. While our focus is to present new co...
Many discrete response variables have counts as possible outcomes. Poisson regression has been recog...
A variety of methods of modelling overdispersed count data are compared. The methods are classified ...
<p><em>Poisson regression is a nonlinear regression that is often used to model count response varia...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
Poisson regression model is one of nonlinear regression model that used to analyze count data, where...
A natural approach to analyzing the effect of covariates on a count response variable is to use a P...
AbstractOverdispersion in time series of counts is very common and has been well studied by many aut...
We present new Stata commands for estimating several regression models suitable for analyzing overdi...
It is common for time series of unbounded counts (that is, nonnegative integers) to display overdisp...
We present motivation and new commands for modeling heaped count data. These data may appear when su...
We frequently encounter outcomes of count that have extra variation. This paper considers several al...
This article describes the R package CountsEPPM and its use in determining maximum likelihood estima...
Abstract: This paper represents the comparison between Negative Binomial Regression model and Genera...
We present a novel distribution for modelling count data that are underdispersed relative to the Poi...
We present motivation and new commands for modeling count data. While our focus is to present new co...
Many discrete response variables have counts as possible outcomes. Poisson regression has been recog...
A variety of methods of modelling overdispersed count data are compared. The methods are classified ...
<p><em>Poisson regression is a nonlinear regression that is often used to model count response varia...
In this paper we consider regression models for count data allowing for overdispersion in a Bayesian...
Poisson regression model is one of nonlinear regression model that used to analyze count data, where...
A natural approach to analyzing the effect of covariates on a count response variable is to use a P...
AbstractOverdispersion in time series of counts is very common and has been well studied by many aut...
We present new Stata commands for estimating several regression models suitable for analyzing overdi...
It is common for time series of unbounded counts (that is, nonnegative integers) to display overdisp...
We present motivation and new commands for modeling heaped count data. These data may appear when su...
We frequently encounter outcomes of count that have extra variation. This paper considers several al...
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