Alle im vorigen Kapitel beschriebenen Problemstellungen besitzen eine wesentliche
Gemeinsamkeit: Eigenschaften einer Zielvariablen y sollen in Abhängigkeit von Kovariablen …

Bayesian variable selection has experienced substantial developments over the past 30
years with the proliferation of large data sets. Identifying relevant variables to include in a …

The scientific rigor and computational methods of causal inference have had great impacts
on many disciplines but have only recently begun to take hold in spatial applications. Spatial …

The study of animal movement has always been a key element in ecological science,
because it is inherently linked to critical processes that scale from individuals to populations …

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation.
Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are …

Many statisticians have had the experience of fitting a linear model with uncorrelated errors,
then adding a spatially-correlated error term (random effect) and finding that the estimates of …

Background Few studies have examined the relationship between traffic noise and
depression providing inconclusive results. This large case-control study is the first to assess …

Plant distributions are strongly influenced by both climate and topography. In an analysis of
geographic and topographic distributions for selected tree species in California, we found …

In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth
are often collinear with spatially smooth random effects. This phenomenon is known as …

Studies in air pollution epidemiology may suffer from some specific forms of confounding
and exposure measurement error. This contribution discusses these, mostly in the …