The EM Algorithm and Extensions (Wiley Series in Probability and Statistics) 1st Edition

by Geoffrey McLachlan (Author), Thriyambakam Krishnan (Author)

3 customer reviews

ISBN-13: 978-0471123583

ISBN-10: 0471123587

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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.

The EM Algorithm and Extensions (Wiley Series in Probability and Statistics)

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Geoffrey McLachlan

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Hardcover

Editorial Reviews

Review

"...should be comprehensible to graduates with statistics as their major subject." (Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001)

From the Publisher

A unified and complete treatment of the theory and methodology of the Expectation-Maximization (EM) algorithm, its extensions and their applications. Applications in standard statistical contexts such as regression, factor analysis, variance-components estimation, repeated-measures designs, categorical data analysis, survival evaluatio, and survey sampling are covered, as well as applications in other areas like genetics and psychometry. Approximately 30 examples illustrate the theory and methodology.

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Product Details

Series: Wiley Series in Probability and Statistics (Book 249)
Hardcover: 304 pages
Publisher: Wiley-Interscience; 1 edition (November 15, 1996)
Language: English
ISBN-10: 0471123587
ISBN-13: 978-0471123583
Product Dimensions: 6.5 x 0.8 x 9.4 inches
Shipping Weight: 1.2 pounds
Average Customer Review: 2.2 out of 5 stars See all reviews (3 customer reviews)
Amazon Best Sellers Rank: #2,138,666 in Books (See Top 100 in Books)
- #4836 in Books > Textbooks > Science & Mathematics > Mathematics > Statistics
- #7192 in Books > Science & Math > Mathematics > Applied > Probability & Statistics

Customer Reviews

2.2 out of 5 stars

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Top Customer Reviews

The campaign for mathematical clarity starts here...

By Count Ludwig on July 9, 2007

Format: Hardcover

These truths I hold to be self evident:
1) It is unacceptable to provide equations without explaining all the symbols in them.
2) If you explain something to an intelligent person and they still don't understand then it is your fault not theirs.
3) Laziness is the right of the reader, not the author.

In practice you assume your audience knows some things, ellide from previous equations for space and fluency, and provide a glossary. But I have a degree in maths (not stats) and still I can't make head or tail of the first two pages of chapter 2 in the excerpt given. So I will look for a book, article or course that assumes less knowledge on my part.

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Excellent text

By A Customer on May 17, 2000

Format: Hardcover

Excellent text on the EM algorithm. Covers theory as well as a number of applications. Clearly written. Historical accounts and examples make reading delightful. I would have found it sweeter if it covered applications in time series. It was only inevitable that everyone's favorite application couldn't be included because of their sheer multitude.
I guess this is also the only text available on the subject, as of now!

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Learn about EM? Read the relevant papers but not this book

By A Customer on November 21, 1999

Format: Hardcover

I tried to read the whole introductive chapter a couple of times but I couldn't understand what is EM about, the used terminology and the basic definitions. The authors say that the book is for theoriticians and practicioners, but I do think it is not appropriate for both categories, unless the reader has been involved in writing papers on this topic. I have enough background knowledge in probability theory and in mathematics but it seems that I have to read all the relevant literature before going a step ahead. In my opinion this book is wide useless for people who do not know EM algorithm.

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