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The prominence of finite mixture modelling is greater than ever. Many important statistical topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity involve finite mixture models in some way or other. The area of potential applications goes beyond simple data analysis and extends to regression analysis and to non-linear time series analysis using Markov switching models.For more than the hundred years since Karl Pearson showed in 1894 how to estimate the five parameters of a mixture of two normal distributions using the method of moments, statistical inference for finite mixture models has been a challenge to everybody who deals with them. In the past ten years, very powerful computational tools emerged for dealing with these models which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book reviews these techniques and covers the most recent advances in the field, among them bridge sampling techniques and reversible jump Markov chain Monte Carlo methods. It is the first time that the Bayesian perspective of finite mixture modelling is systematically presented in book form. It is argued that the Bayesian approach provides much insight in this context and is easily implemented in practice. Although the main focus is 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 aim of this book is to impart the finite mixture and Markov switching approach to statistical modelling to a wide-ranging community. This includes not only statisticians, but also biologists, economists, engineers, financial agents, market researcher, medical researchers or any other frequent user of statistical models. This book should help newcomers to the field to understand how finite mixture and Markov switching models are formulated, what structures they imply on the data, what they could be used for, and how they are estimated. Researchers familiar with the subject also will profit from reading this book. The presentation is rather informal without abandoning mathematical correctness. Previous notions of Bayesian inference and Monte Carlo simulation are useful but not needed.

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Finite Mixture and Markov Switching Models - Springer ~ Many important statistical topics like clustering data, outlier treatment, or dealing with unobserved heterogeneity involve finite mixture models in some way or other. The area of potential applications goes beyond simple data analysis and extends to regression analysis and to non-linear time series analysis using Markov switching models.

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Finite Mixture and Markov Switching Models ~ Sylvia Fruh˜ wirth-Schnatter Finite Mixture and Markov Switching Models Implementation in MATLAB using the package bayesf Version 2.0 December 2, 2008

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Finite Mixture And Markov Switching Models Springer Series ~ Finite Mixture and Markov Switching Models (Springer Series in Statistics) - Kindle edition by Frühwirth-Schnatter, Sylvia. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Finite Mixture and Markov Switching Models (Springer Series in Statistics).

Finite Mixture and Markov Switching Models - Springer ~ "Finite mixture distributions are important for many models. Therefore they constitute a very active field of research. This book gives an up to date overview over the various models of this kind. … The aim of this book is to impart the finite mixture and Markov switching approach to statistical modeling to a wide-ranging community. …

Finite Mixture Modeling / SpringerLink ~ Finite Mixture and Markov Switching Models. Finite Mixture and Markov Switching Models pp 1-23 . Finite Mixture Modeling. Chapter. 3.4k Downloads; Part of the Springer Series in Statistics book series . (2006) Finite Mixture Modeling. In: Finite Mixture and Markov Switching Models. Springer Series in Statistics. Springer, New York, NY .

Finite Markov Mixture Modeling / SpringerLink ~ Cite this chapter as: (2006) Finite Markov Mixture Modeling. In: Finite Mixture and Markov Switching Models. Springer Series in Statistics.

Finite Mixtures of Regression Models - Home - Springer ~ Cite this chapter as: (2006) Finite Mixtures of Regression Models. In: Finite Mixture and Markov Switching Models. Springer Series in Statistics.

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Finite Mixture Models with Nonnormal Components - Springer ~ Cite this chapter as: (2006) Finite Mixture Models with Nonnormal Components. In: Finite Mixture and Markov Switching Models. Springer Series in Statistics.

Finite Mixture and Markov Switching Models (Springer ~ 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.

Finite mixture and Markov switching models (eBook, 2006 ~ Finite mixture and Markov switching models. . powerful computational tools for modeling which combine a Bayesian approach with Monte simulation techniques based on Markov chains. This book reviews these techniques and covers advances in the field. . # Springer series in statistics.\/span>\n \n schema: .

Finite Mixture and Markov Switching Models (Springer ~ Finite Mixture and Markov Switching Models (Springer Series in Statistics) eBook: Sylvia Frühwirth-Schnatter: .ca: Kindle Store

Finite Mixture and Markov Switching Models (Springer ~ 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.

Finite Mixture and Markov Switching Models / Computer ~ Get and download textbook Finite Mixture and Markov Switching Models (Springer Series in Statistics) for free This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modeling, showing how finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated.

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