This paper provides a practical simulation-based Bayesian analysis of parameter-driven models for time series Poisson data with the AR(1) latent process. The posterior distribution is simulated by a Gibbs sampling algorithm. Full conditional posterior distributions of unknown variables in the model are given in convenient forms for the Gibbs sampling algorithm. The case with missing observations is also discussed. The methods are applied to real polio data from 1970 to 1983.
We propose modeling for Poisson processes over time, exploiting the connection of the Poisson proces...
The Poisson regression model for count data belongs to the family of “generalized linear models”, an...
This paper surveys recently developed methods for Bayesian inference and their use in economic time ...
Time series involving count data are present in a wide variety of applications. In many application...
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with ex...
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel...
textabstractSeveral lessons learned from a Bayesian analysis of basic economic time series models by...
1 Gibbs algorithm We detail the algorithm used to sample from the posterior distribution (λ,A, γ)|N ...
In time series analysis, latent factors are often introduced to model the heterogeneous time evoluti...
We examine autoregressive time series models that are subject to regime switching. These shifts are ...
In time series analysis, latent factors are often introduced to model the heterogeneous time evoluti...
This paper focuses on the Bayesian posterior mean estimates (or Bayes' estimate) of the parameter se...
This paper looks into the Bayesian approach for analyzing and selecting the best Poisson process mod...
Poisson log-linear models are ubiquitous in many applications, and one of the most popular approache...
International audienceCombining extreme-value theory with Bayesian methods offers several advantages...
We propose modeling for Poisson processes over time, exploiting the connection of the Poisson proces...
The Poisson regression model for count data belongs to the family of “generalized linear models”, an...
This paper surveys recently developed methods for Bayesian inference and their use in economic time ...
Time series involving count data are present in a wide variety of applications. In many application...
In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with ex...
This paper is concerned with the problems of posterior simulation and model choice for Poisson panel...
textabstractSeveral lessons learned from a Bayesian analysis of basic economic time series models by...
1 Gibbs algorithm We detail the algorithm used to sample from the posterior distribution (λ,A, γ)|N ...
In time series analysis, latent factors are often introduced to model the heterogeneous time evoluti...
We examine autoregressive time series models that are subject to regime switching. These shifts are ...
In time series analysis, latent factors are often introduced to model the heterogeneous time evoluti...
This paper focuses on the Bayesian posterior mean estimates (or Bayes' estimate) of the parameter se...
This paper looks into the Bayesian approach for analyzing and selecting the best Poisson process mod...
Poisson log-linear models are ubiquitous in many applications, and one of the most popular approache...
International audienceCombining extreme-value theory with Bayesian methods offers several advantages...
We propose modeling for Poisson processes over time, exploiting the connection of the Poisson proces...
The Poisson regression model for count data belongs to the family of “generalized linear models”, an...
This paper surveys recently developed methods for Bayesian inference and their use in economic time ...
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