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Editorial Reviews
Review
"...they are to be congratulated on the extent of their achievement..." -- The Statistician, Vol.51, No.3
"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)
"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)
Product Description
An up-to-date, comprehensive account of major issues in finite mixture modeling
This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts.
Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
* Provides more than 800 references-400ublished since 1995
* Includes an appendix listing available mixture software
* Links statistical literature with machine learning and pattern recognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data.
This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts.
Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide:
* Provides more than 800 references-400ublished since 1995
* Includes an appendix listing available mixture software
* Links statistical literature with machine learning and pattern recognition literature
* Contains more than 100 helpful graphs, charts, and tables
Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data.
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Inside This Book
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First Sentence:
Finite mixtures of distributions have provided a mathematical-based approach to the statistical modeling of a wide variety of random phenomena. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
spurious local maximizers, partial nonrandom classification, heteroscedastic components, normal mixture model, univariate normal components, ith component density, empirical information matrix, factor analyzers, bth block, mixture model fitted, component membership, finite mixture models, univariate normal mixtures, normal component densities, conditional survival function, crab data, normal mixture density, unrestricted variances, mixture software, same parametric family, normal mixture densities, observed information matrix, scale matrices, posterior simulation, mixing proportions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Journal of the American Statistical Association, Journal of the Royal Statistical Society, Monte Carlo, New York, Technical Report, Applied Statistics, Annals of Statistics, Canadian Journal of Statistics, Synthetic Data Set, Journal of Classification, Probability Letters, Annals of Mathematical Statistics, Statistical Methods, Neural Information Processing Systems, The Astrophysical Journal, Cambridge University Press, Morgan Kaufmann, Neural Computation, Oxford University Press, Statistica Sinica, The Astronomical Journal, Times Overall, University of California, Australian Journal of Statistics, Department of Statistics New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Finite mixtures of distributions have provided a mathematical-based approach to the statistical modeling of a wide variety of random phenomena. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
spurious local maximizers, partial nonrandom classification, heteroscedastic components, normal mixture model, univariate normal components, ith component density, empirical information matrix, factor analyzers, bth block, mixture model fitted, component membership, finite mixture models, univariate normal mixtures, normal component densities, conditional survival function, crab data, normal mixture density, unrestricted variances, mixture software, same parametric family, normal mixture densities, observed information matrix, scale matrices, posterior simulation, mixing proportions Key Phrases - Capitalized Phrases (CAPs): (learn more)
Journal of the American Statistical Association, Journal of the Royal Statistical Society, Monte Carlo, New York, Technical Report, Applied Statistics, Annals of Statistics, Canadian Journal of Statistics, Synthetic Data Set, Journal of Classification, Probability Letters, Annals of Mathematical Statistics, Statistical Methods, Neural Information Processing Systems, The Astrophysical Journal, Cambridge University Press, Morgan Kaufmann, Neural Computation, Oxford University Press, Statistica Sinica, The Astronomical Journal, Times Overall, University of California, Australian Journal of Statistics, Department of Statistics New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
See all 100 books this book cites
100
books
cite this book:
See all 100 books citing this book
- Probability and Statistics by Dale M. Johnson in Front Matter, Back Matter (1), and Back Matter (2)
- The Statistical Analysis of Failure Time Data (Wiley Series in Probability and Statistics) by John D. Kalbfleisch on page 372, and Back Matter
- Order Statistics (Wiley Series in Probability and Statistics) by Herbert A. David in Back Matter (1), and Back Matter (2)
- Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley Series in Probability and Statistics) by David W. Scott in Back Matter
- Case Studies in Biometry by Nicholas Lange in Back Matter
See all 100 books this book cites
- Regression Models for Time Series Analysis (Wiley Series in Probability and Statistics) by Benjamin Kedem in Back Matter (1), and Back Matter (2)
- Applied Latent Class Analysis by Jacques A. Hagenaars on page 82, and Back Matter
- Financial Cryptography and Data Security: 9th International Conference, FC 2005, Roseau, The Commonwealth Of Dominica, February 28 - March 3, 2005, Revised ... Computer Science / Security and Cryptology) by Andrew S. Patrick on page 41, and page 51
- Random Graphs for Statistical Pattern Recognition (Wiley Series in Probability and Statistics) by David J. Marchette in Back Matter
- Exploratory Data Mining and Data Cleaning by Tamraparni Dasu in Back Matter
See all 100 books citing this book
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