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Author | : Sylvia Frühwirth-Schnatter |
Publisher | : Springer Science & Business Media |
Total Pages | : 506 |
Release | : 2006-11-24 |
Genre | : Mathematics |
ISBN | : 0387357688 |
Download Finite Mixture and Markov Switching Models Book in PDF, ePub and Kindle
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.
Author | : Sylvia Frühwirth-Schnatter |
Publisher | : Springer |
Total Pages | : 494 |
Release | : 2006-08-08 |
Genre | : Mathematics |
ISBN | : 9780387329093 |
Download Finite Mixture and Markov Switching Models Book in PDF, ePub and Kindle
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.
Author | : Geoffrey McLachlan |
Publisher | : John Wiley & Sons |
Total Pages | : 419 |
Release | : 2004-03-22 |
Genre | : Mathematics |
ISBN | : 047165406X |
Download Finite Mixture Models Book in PDF, ePub and Kindle
An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Download MCMC Estimation of Classical and Dynamic Switching and Mixture Models Book in PDF, ePub and Kindle
In the present paper we discuss Bayesian estimation of a very general model class where the distribution of the observations is assumed to depend on a latent mixture or switching variable taking values in a discrete state space. This model class covers e.g. finite mixture modelling, Markov switching autoregressive modelling and dynamic linear models with switching. Joint Bayesian estimation of all latent variables, model parameters and parameters determining the probability law of the switching variable is carried out by a new Markov Chain Monte Carlo method called permutation sampling. Estimation of switching and mixture models is known to be faced with identifiability problems as switching and mixture are identifiable only up to permutations of the indices of the states. For a Bayesian analysis the posterior has to be constrained in such a way that identifiablity constraints are fulfilled. The permutation sampler is designed to sample efficiently from the constrained posterior, by first sampling from the unconstrained posterior - which often can be done in a convenient multimove manner - and then by applying a suitable permutation, if the identifiability constraint is violated. We present simple conditions on the prior which ensure that this method is a valid Markov Chain Monte Carlo method (that is invariance, irreducibility and aperiodicity hold). Three case studies are presented, including finite mixture modelling of fetal lamb data, Markov switching Autoregressive modelling of the U.S. quarterly real GDP data, and modelling the U .S./U.K. real exchange rate by a dynamic linear model with Markov switching heteroscedasticity. (author's abstract).
Author | : Paul Victor Hamilton |
Publisher | : |
Total Pages | : 382 |
Release | : 2002 |
Genre | : |
ISBN | : |
Download Panel Markov-switching Models of Economic Phenomena Book in PDF, ePub and Kindle
Author | : D. M. Titterington |
Publisher | : |
Total Pages | : 264 |
Release | : 1985 |
Genre | : Mathematics |
ISBN | : |
Download Statistical Analysis of Finite Mixture Distributions Book in PDF, ePub and Kindle
In this book, the authors give a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions.
Author | : Greg N. Gregoriou |
Publisher | : Springer |
Total Pages | : 214 |
Release | : 2010-12-08 |
Genre | : Business & Economics |
ISBN | : 0230295215 |
Download Nonlinear Financial Econometrics: Markov Switching Models, Persistence and Nonlinear Cointegration Book in PDF, ePub and Kindle
This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new evidence and solutions to capture the persistence observed in stock returns across developed and emerging markets.
Author | : Kerrie L. Mengersen |
Publisher | : John Wiley & Sons |
Total Pages | : 357 |
Release | : 2011-05-03 |
Genre | : Mathematics |
ISBN | : 1119998441 |
Download Mixtures Book in PDF, ePub and Kindle
This book uses the EM (expectation maximization) algorithm to simultaneously estimate the missing data and unknown parameter(s) associated with a data set. The parameters describe the component distributions of the mixture; the distributions may be continuous or discrete. The editors provide a complete account of the applications, mathematical structure and statistical analysis of finite mixture distributions along with MCMC computational methods, together with a range of detailed discussions covering the applications of the methods and features chapters from the leading experts on the subject. The applications are drawn from scientific discipline, including biostatistics, computer science, ecology and finance. This area of statistics is important to a range of disciplines, and its methodology attracts interest from researchers in the fields in which it can be applied.
Author | : Sylvia Fruhwirth-Schnatter |
Publisher | : CRC Press |
Total Pages | : 388 |
Release | : 2019-01-04 |
Genre | : Computers |
ISBN | : 0429508867 |
Download Handbook of Mixture Analysis Book in PDF, ePub and Kindle
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
Author | : Sylvia Fruhwirth-Schnatter |
Publisher | : CRC Press |
Total Pages | : 522 |
Release | : 2019-01-04 |
Genre | : Computers |
ISBN | : 0429508247 |
Download Handbook of Mixture Analysis Book in PDF, ePub and Kindle
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.