Verwandte Artikel zu Finite Mixture and Markov Switching Models (Springer...
Inhaltsangabe
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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Von der hinteren Coverseite
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 andMarkov 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.
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Neu kaufen
Diesen Artikel anzeigen
EUR 112,67
EUR 42,60 für den Versand von USA nach Deutschland
Versandziele, Kosten & Dauer
Suchergebnisse für Finite Mixture and Markov Switching Models (Springer...
Foto des Verkäufers
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: thebookforest.com, San Rafael, CA, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010. Bestandsnummer des Verkäufers 1LAUHV002CKR
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: KuleliBooks, Phoenix, AZ, USA
Verkäuferbewertung 5 von 5 Sternen
hardcover. Zustand: Good. The book may have minor cosmetic wear (i.e. creased spine/cover, scratches, curled corners, folded pages, minor sunburn, minor water damage, minor bent). The book may have some highlights/notes/underlined pages - Accessories such as CD, codes, toys, may not be included - Safe and Secure Mailer - No Hassle Return. Bestandsnummer des Verkäufers 521YH0000WSX
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models
Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. pp. 516. Bestandsnummer des Verkäufers 18280736
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: Bahamut Media, Reading, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Hardcover. Zustand: Very Good. This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. Bestandsnummer des Verkäufers 6545-9780387329093
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: AwesomeBooks, Wallingford, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Hardcover. Zustand: Very Good. Finite Mixture and Markov Switching Models (Springer Series in Statistics) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. Bestandsnummer des Verkäufers 7719-9780387329093
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: BennettBooksLtd, San Diego, NV, USA
Verkäuferbewertung 5 von 5 Sternen
hardcover. Zustand: New. In shrink wrap. Looks like an interesting title! Bestandsnummer des Verkäufers Q-0387329099
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In English. Bestandsnummer des Verkäufers ria9780387329093_new
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
Finite Mixture and Markov Switching Models
Print-on-DemandAnbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 andMarkov 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.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. 494 pp. Englisch. Bestandsnummer des Verkäufers 9780387329093
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Finite Mixture and Markov Switching Models (Springer Series in Statistics)
Anbieter: Best Price, Torrance, CA, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. SUPER FAST SHIPPING. Bestandsnummer des Verkäufers 9780387329093
Anzahl: 2 verfügbar
Foto des Verkäufers
Finite Mixture and Markov Switching Models
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 andMarkov 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.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. Bestandsnummer des Verkäufers 9780387329093
Anzahl: 2 verfügbar