The EM Algorithm and Extensions (Wiley Series in Probability and Statistics)
Language: English
Published by Wiley-Interscience, 1996
Series: Book 156 of 358 - Wiley Series in Probability and Statistics
- Hardcover
- Used
Seller: Austin Goodwill 1101, Austin, TX, U.S.A.Austin Goodwill 1101
AbeBooks seller since May 21, 2021
Condition: Used - Good
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Book shows general signs of use and handling. May have light wear on the cover or edges and minimal writing or highlighting. Binding remains tight, and pages are clean and readable.
Seller Inventory # CTXV.0471123587.G
- Title
- The EM Algorithm and Extensions (Wiley Series in Probability and Statistics)
- Author
- McLachlan, Geoffrey; Krishnan, Thriyambakam
- Publisher
- Wiley-Interscience
- Publication year
- 1996
- Condition
- good
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0471123587
- ISBN 13
- 9780471123583
- Series
- Book 156 of 358: Wiley Series in Probability and Statistics
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Austin Goodwill 1101
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