| 書(shū)目名稱(chēng) | Finite Mixture and Markov Switching Models |
| 編輯 | Sylvia Frühwirth-Schnatter |
| 視頻video | http://file.papertrans.cn/344/343634/343634.mp4 |
| 概述 | Mixture models are nowadays applied in many different areas such as biometrics, medicine, marketing whereas switching models are applied essentially in economics and finance |
| 叢書(shū)名稱(chēng) | Springer Series in Statistics |
| 圖書(shū)封面 |  |
| 描述 | .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 B |
| 出版日期 | Book 2006 |
| 關(guān)鍵詞 | Markov chain; Monte Carlo Method; Monte Carlo Simulation; Normal distribution; Regression analysis; Simul |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-0-387-35768-3 |
| isbn_softcover | 978-1-4419-2194-9 |
| isbn_ebook | 978-0-387-35768-3Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2006 |
|Archiver|手機(jī)版|小黑屋|
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