A new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors is proposed. In contrast to many known selection criteria, the method is based on an interpretable scaled quantity. This quantity measures a maximal relative error one...

Recently an efficient fixed point algorithm, called maximization by parts (MBP), for finding maximum likelihood estimates has been applied to models based on Gaussian copulas. It requires a decomposition of a likelihood function into two parts and their iterative maximization by solving score...

We provide a Bayesian analysis of pair-copula constructions (PCCs) (Aas et al., 2009), which outperform many other multivariate copula constructions in modeling dependencies in financial data. We use bivariate t-copulas as building blocks in a PCC to allow extreme events in bivariate margins...

Vine pair-copula constructions (PCCs) provide an important milestone for the usage of multivariate copulas to model dependence. At present time PCCs are recognized to be the most flexible class of multivariate copulas. Vine PCCs and semiparametric copula-based dynamic (SCOMDY) models with...

This paper focuses on an extension of zero-inflated generalized Poisson (ZIGP) regression models for count data. We discuss generalized Poisson (GP) models where dispersion is modelled by an additional model parameter. Moreover, zero-inflated models in which overdispersion is assumed to be...

A new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability. The test relies on asymptotic normality of the test statistic, and therefore...

Poisson regression models for count variables have been utilized in many applications. However, in many problems overdispersion and zeroinflation occur. We study in this paper regression models based on the generalized Poisson distribution (Consul (1989)). These regression models which have been...

A new method for testing linear restrictions in linear regression models is suggested. It allows to validate the linear restriction, up to a specified approximation error and with a specified error probability. The test relies on asymptotic normality of the test statistic, and therefore...