Finite Mixture and Markov Switching Models

2006. Auflage 2006
ISBN: 978-0-387-32909-3
Verlag: Springer

Buch, Englisch, 494 Seiten, Format (B × H): 159 mm x 244 mm, Gewicht: 1980 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 is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show 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, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.

Zielgruppe

Researchers

Autoren/Hrsg.

Frühwirth-Schnatter, Sylvia

Fachgebiete

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

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.