Add to ORKGThe Poisson regression model for count data belongs to the family of “generalized linear models”, and is available in the R system for statistical computing. In this article, the Bayesian methods are applied to fit the Poisson model using analytic and simulation tools. Laplace Approximation is implemented for approximating posterior densities of the parameters. Moreover, parallel simulation tools are implemented using LaplacesDemon and R2jags packages of R. A data set “DoctorVisits” is used for the purpose of illustrations
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likeliho...
Bayesian linear regression is an approach to linear regression where statistical analysis depend of ...
This paper provides a practical simulation-based Bayesian analysis of parameter-driven models for ti...
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with ex...
There has been a dramatic growth in the development and application of Bayesian inferential methods....
This article describes the R package CountsEPPM and its use in determining maximum likelihood estima...
Time series involving count data are present in a wide variety of applications. In many application...
Recent advances in big data and analytics research have provided a wealth of large data sets that ar...
Background: Many recent statistical applications involve inference under complex models, where it is...
Recent advances in big data and analytics research have provided a wealth of large data sets that ar...
The traditional Poisson regression model for fitting count data is considered inadequate to fit over...
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictio...
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel...
This paper develops semiparametric Bayesian estimation approach for Poisson regression models with u...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likeliho...
Bayesian linear regression is an approach to linear regression where statistical analysis depend of ...
This paper provides a practical simulation-based Bayesian analysis of parameter-driven models for ti...
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with ex...
There has been a dramatic growth in the development and application of Bayesian inferential methods....
This article describes the R package CountsEPPM and its use in determining maximum likelihood estima...
Time series involving count data are present in a wide variety of applications. In many application...
Recent advances in big data and analytics research have provided a wealth of large data sets that ar...
Background: Many recent statistical applications involve inference under complex models, where it is...
Recent advances in big data and analytics research have provided a wealth of large data sets that ar...
The traditional Poisson regression model for fitting count data is considered inadequate to fit over...
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictio...
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel...
This paper develops semiparametric Bayesian estimation approach for Poisson regression models with u...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Fo...
The key quantity needed for Bayesian hypothesis testing and model selection is the marginal likeliho...
Bayesian linear regression is an approach to linear regression where statistical analysis depend of ...