In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

The book's website (with databases and other support materials) can be accessed here.
Praise for the Second Edition: The second edition introduces an especially broad set of …

The fourth edition follows the general layout of the third edition but includes some
modernization of topics as well as the coverage of additional topics. The preface to the third …

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a
unified, practical likelihood-based framework for the analysis of stochastic volatility models …

This article analyses the recently suggested particle approach to filtering time series. We
suggest that the algorithm is not robust to outliers for two reasons: The design of the …

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

New techniques for the analysis of stochastic volatility models in which the logarithm of
conditional variance follows an autoregressive model are developed. A cyclic Metropolis …

In this course we perform an introduction to Bayesian methods, we show some basic
definitions and properties of the bayesian approach. We have taken the content from the …

This book contains an up-to-date coverage of the last twenty years advances in Bayesian
inference in econometrics, with an emphasis on dynamic models. It shows how to treat …

This paper considers some recent developments in non‐linear and linear time series
analysis. It consists of two main components. The first emphasizes the advances in non …