Finite Mixture Models

Geoffrey J. McLachlan (Author), David Peel (Author)

9780471006268, Wiley

Hardback, published 1 November 2000

464 pages
23.6 x 15.8 x 2.8 cm, 0.771 kg

"This is an excellent book.... I enjoyed reading this book. I recommend it highly to both mathematical and applied statisticians." (Technometrics, February 2002)
"This book will become popular to many researchers...the material covered is so wide that it will make this book a standard reference for the forthcoming years." (Zentralblatt MATH, Vol. 963, 2001/13)
"the material covered is so wide that it will make this book a standard reference for the forthcoming years." (Zentralblatt MATH, Vol.963, No.13, 2001)
"This book is excellent reading...should also serve as an excellent handbook on mixture modelling..." (Mathematical Reviews, 2002b)
"...contains valuable information about mixtures for researchers..." (Journal of Mathematical Psychology, 2002)
"...a masterly overview of the area...It is difficult to ask for more and there is no doubt that McLachlan and Peel's book will be the standard reference on mixture models for many years to come." (Statistical Methods in Medical Research, Vol. 11, 2002)
"...they are to be congratulated on the extent of their achievement..." (The Statistician, Vol.51, No.3)

Im hier beschriebenen Modell wird die Verteilung einer Zufallsgröße als Mischung einer endlichen Zahl von Komponentenverteilungen in verschiedenen Verhältnissen behandelt. Die Verhältnisse sind dabei nichtnegativ und summieren sich zu eins. Typische Einsatzgebiete dieses Ansatzes bestehen in Populationen heterogener Zusammensetzung, beispielsweise bei klinischen Versuchen oder Zuverlässigkeitsprüfungen in der Technik. Die Autoren legen Wert auf die praktischen Aspekte des Verfahrens; einschlägige Software läßt sich aus den Anhängen entnehmen. (12/00)

General Introduction.

ML Fitting of Mixture Models.

Multivariate Normal Mixtures.

Bayesian Approach to Mixture Analysis.

Mixtures with Nonnormal Components.

Assessing the Number of Components in Mixture Models.

Multivariate t Mixtures.

Mixtures of Factor Analyzers.

Fitting Mixture Models to Binned Data.

Mixture Models for Failure-Time Data.

Mixture Analysis of Directional Data.

Variants of the EM Algorithm for Large Databases.

Hidden Markov Models.

Appendices.

References.

Indexes.

Subject Areas: Mathematics [PB]