This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C
routines for carrying out computations involving the statistical analysis of univariate and …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics-a
very active area of research in which few up-to-date reference works are available. This is …

We show how to use the Gibbs sampler to carry out Bayesian inference on a linear state
space model with errors that are a mixture of normals and coefficients that can switch over …

Since our first edition of this book, many developments in statistical mod elling based on
generalized linear models have been published, and our primary aim is to bring the book up …

Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

Most regression problems in practice require flexible semiparametric forms of the predictor
for modelling the dependence of responses on covariates. Moreover, it is often necessary to …

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific
computing. This book discusses recent developments of MCMC methods with an emphasis …

The Kalman recursion for state space models is extended to allow for likelihood evaluation
and minimum mean square estimation given states with an arbitrarily large covariance …

Statistics and computing share many close relationships. Computing now permeates every
aspect of statistics, from pure description to the development of statistical theory. At the same …