The software for spatial econometrics available in the R system for statistical computing is
reviewed. The methods are illustrated in a historical perspective, highlighting the main lines …

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist
should know before they start working with spatial data. These aspects include how …

This article discusses a general framework for smoothing parameter estimation for models
with regular likelihoods constructed in terms of unknown smooth functions of covariates …

The first edition of this book has established itself as one of the leading references on
generalized additive models (GAMs), and the only book on the topic to be introductory in …

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting
a broad class of Bayesian regression models. No samples of the posterior marginal …

Spatial Regression Models illustrates the use of spatial analysis in the social sciences within
a regression framework and is accessible to readers with no prior background in spatial …

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

Regression models in which a response variable is related to smooth functions of some
predictor variables are popular as a result of their appealing balance between flexibility and …

Bayesian analysis provides a convenient setting for the estimation of complex generalized
additive regression models (GAMs). Since computational power has tremendously …

This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-
splines combine regression on B-splines with simple, discrete, roughness penalties. They …