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 …

Publisher Summary Structural time series model is one which is set up in terms of
components, which have a direct interpretation. Thus, for example, one may consider the …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

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 …

A simulation smoother in state space time series analysis is a procedure for drawing
samples from the conditional distribution of state or disturbance vectors given the …

Recently suggested procedures for simulating from the posterior density of states given a
Gaussian state space time series are refined and extended. We introduce and study the …

Diagnostic checking of the specification of time series models is normally carried out using
the innovations—that is, the one-step-ahead prediction errors. In an unobserved …

This is the second volume of a text on the theory and practice of maximum penalized
likelihood estimation. It is intended for graduate students in statistics, operations research …

This article presents a new exact solution for the initialization of the Kalman filter for state
space models with diffuse initial conditions. For example, the regression model with …