Multivariate count data occur in several different disciplines. However, existing models do not offer great flexibility for dependence modeling. Models based on copulas nowadays are widely used for continuous data dependence modeling. Modeling count data via copulas is still in its infancy; see the recent article of Genest and Nešlehová (2007). A series of different copula models providing various residual dependence structures are considered for vectors of count response variables whose marginal distributions depend on covariates through negative binomial regressions. A real data application related to the number of purchases of different products is provided
An important issue in multivariate statistical modeling is the choice of the appropriate dependence ...
Almost all existing nonlinear multivariate time series models remain linear, conditional on a point ...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
In this project we introduce the use of copulas for dealing with residual dependencies in item respo...
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field ...
In this chapter, we review the problem of modeling correlated count data. Among the several methods ...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
In this research we introduce a new class of multivariate probability models to the marketing litera...
Understanding and quantifying dependence is at the core of all modelling efforts in the areas of ins...
Copulas are mathematical objects that fully capture the dependence structure among random variables ...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
Multivariate volatility models, such as DCC MGARCH, are estimated under assumption of multivariate n...
An important issue in multivariate statistical modeling is the choice of the appropriate dependence ...
Almost all existing nonlinear multivariate time series models remain linear, conditional on a point ...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
In many cases of modeling bivariate count data, the interest lies on studying the association rather...
In this project we introduce the use of copulas for dealing with residual dependencies in item respo...
Dependence Modeling with Copulas covers the substantial advances that have taken place in the field ...
In this chapter, we review the problem of modeling correlated count data. Among the several methods ...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...
In this research we introduce a new class of multivariate probability models to the marketing litera...
Understanding and quantifying dependence is at the core of all modelling efforts in the areas of ins...
Copulas are mathematical objects that fully capture the dependence structure among random variables ...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
Copula modelling is becoming more popular in modelling dependence multivariate distributions and var...
Multivariate volatility models, such as DCC MGARCH, are estimated under assumption of multivariate n...
An important issue in multivariate statistical modeling is the choice of the appropriate dependence ...
Almost all existing nonlinear multivariate time series models remain linear, conditional on a point ...
We are studying linear and log-linear models for multivariate count time series data with Poisson ma...