Finite Mixture Models (Wiley Series in Probability and Statistics) by Geoffrey J. McLachlan |
by Robert Tibshirani
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Finite Mixture and Markov Switching Models (Springer Series in Statistics) by Sylvia Frühwirth-Schnatter |
Monte Carlo Statistical Methods (Springer Texts in Statistics) by Christian P. Robert |
Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman |
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Editorial Reviews
Review
"...should be comprehensible to graduates with statistics as their major subject." (Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001)
Product Description
The first unified account of the theory, methodology, and applications of the EM algorithm and its extensions
Since its inception in 1977, the Expectation-Maximization (EM) algorithm has been the subject of intense scrutiny, dozens of applications, numerous extensions, and thousands of publications. The algorithm and its extensions are now standard tools applied to incomplete data problems in virtually every field in which statistical methods are used. Until now, however, no single source offered a complete and unified treatment of the subject.
The EM Algorithm and Extensions describes the formulation of the EM algorithm, details its methodology, discusses its implementation, and illustrates applications in many statistical contexts. Employing numerous examples, Geoffrey McLachlan and Thriyambakam Krishnan examine applications both in evidently incomplete data situations—where data are missing, distributions are truncated, or observations are censored or grouped—and in a broad variety of situations in which incompleteness is neither natural nor evident. They point out the algorithm's shortcomings and explain how these are addressed in the various extensions.
Areas of application discussed include:
- Regression
- Medical imaging
- Categorical data analysis
- Finite mixture analysis
- Factor analysis
- Robust statistical modeling
- Variance-components estimation
- Survival analysis
- Repeated-measures designs
For theoreticians, practitioners, and graduate students in statistics as well as researchers in the social and physical sciences, The EM Algorithm and Extensions opens the door to the tremendous potential of this remarkably versatile statistical tool.
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Inside This Book
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First Sentence:
Before we proceed to present the basic theory underlying the EM algorithm, we give in this chapter a variety of examples to demonstrate how the EM algorithm can be conveniently applied to find the MLE in some commonly occurring situations. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Iterative Proportional Fitting, Journal of the American Statistical Association, Stochastic E-step New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Before we proceed to present the basic theory underlying the EM algorithm, we give in this chapter a variety of examples to demonstrate how the EM algorithm can be conveniently applied to find the MLE in some commonly occurring situations. Read the first page Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Iterative Proportional Fitting, Journal of the American Statistical Association, Stochastic E-step 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
1
book
cites this book:
- Statistical Analysis of Finite Mixture Distributions (Wiley Series in Probability and Statistics) by D. M. Titterington in Back Matter (1), and Back Matter (2)
- Statistical Analysis with Missing Data, Second Edition by Roderick J. A. Little in Back Matter (1), and Back Matter (2)
- Linear Models: Least Squares and Alternatives (Springer Series in Statistics) by C.R. Rao in Back Matter (1), and Back Matter (2)
- Modern Business Statistics (Wiley series in computers & information processing systems for business) by Ronald L. Iman in Back Matter (1), and Back Matter (2)
- Convergence of Probability Measures (Wiley Series in Probability and Statistics) by Patrick Billingsley in Back Matter (1), and Back Matter (2)
See all 100 books this book cites
- Pattern Classification (2nd Edition) by Richard O. Duda on page 160
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