Survival data often contain spatial information, such as the residence. In many cases the
impact of such spatial effects on hazard rates is of considerable interest. We propose flexible …

Mixed model-based estimation of additive or geoadditive regression models has become
popular throughout recent years. It provides a unified and modular framework that facilitates …

Survival models have a long history in the biomedical and biostatistical literature, and are
enormously popular in the analysis of time-to-event data. Very often these data will be …

Mixed model based approaches for semiparametric regression have gained much interest in
recent years, both in theory and application. They provide a unified and modular framework …

In this paper, we introduce a new spatial model that incorporates heteroscedastic variance
depending on neighbouring locations. The proposed process is considered as the spatial …

In health services research, it is common to encounter semicontinuous data characterized by
a point mass at zero and a continuous distribution of positive values. Examples include …

The emergence of geographical information systems and related softwares nowadays
enables medical databases to incorporate the geographical information on patients …

The last decade has witnessed major developments in Geographical Information Systems
(GIS) technology resulting in the need for statisticians to develop models that account for …

We apply a full Bayesian model framework to a dataset on stomach cancer mortality in West
Germany. The data are stratified by age group, year, and district. Using an age–period …

In Bayesian disease mapping, one needs to specify a neighborhood structure to make
inference about the underlying geographical relative risks. We propose a model in which the …