Finite Mixture and Markov Switching Models

1. Auflage. Softcover version of original hardcover Auflage 2006
ISBN: 978-1-4419-2194-9
Verlag: Springer

Buch, Englisch, 494 Seiten, Previously published in hardcover, Format (B × H): 152 mm x 229 mm, Gewicht: 739 g

Reihe: Springer Series in Statistics

The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book reviews these techniques and covers advances in the field. This is the first book to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. Focusing mainly on Bayesian inference, the author reviews several frequentist techniques, especially selecting the number of components of a finite mixture model, and discusses some of their shortcomings compared to the Bayesian approach. The book is designed to show how finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, the book will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Zielgruppe

Research

Autoren/Hrsg.

Frühwirth-Schnatter, Sylvia

Fachgebiete

• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Experimentelle Psychologie
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik EDV | Informatik Informatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Finite Mixture Modeling.- Statistical Inference for a Finite Mixture Model with Known Number of Components.- Practical Bayesian Inference for a Finite Mixture Model with Known Number of Components.- Statistical Inference for Finite Mixture Models Under Model Specification Uncertainty.- Computational Tools for Bayesian Inference for Finite Mixtures Models Under Model Specification Uncertainty.- Finite Mixture Models with Normal Components.- Data Analysis Based on Finite Mixtures.- Finite Mixtures of Regression Models.- Finite Mixture Models with Nonnormal Components.- Finite Markov Mixture Modeling.- Statistical Inference for Markov Switching Models.- Nonlinear Time Series Analysis Based on Markov Switching Models.- Switching State Space Models.

Über Autor(innen)

Sylvia Frühwirth-Schnatter is Professor of Applied Statistics and Econometrics at the Department of Applied Statistics of the Johannes Kepler University in Linz, Austria. She received her Ph.D. in mathematics from the University of Technology in Vienna in 1988. She has published in many leading journals in applied statistics and econometrics on topics such as Bayesian inference, finite mixture models, Markov switching models, state space models, and their application in marketing, economics and finance.