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The Em Algorithm and Extensions

The Em Algorithm and Extensions

          
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The only single-source----now completely updated and revised----to offer a unified treatment of the theory, methodology, and applications of the EM algorithm

Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented.

While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include:

  • New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm

  • New results on convergence, including convergence of the EM algorithm in constrained parameter spaces

  • Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation

  • Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space

  • Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods

  • Plentiful pedagogical elements--chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site

The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

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About the Book

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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Product Details
  • ISBN-13: 9780471123583
  • Publisher: Wiley-Interscience
  • Binding: Hardcover
  • Language: English
  • Series Title: Wiley Series in Probability an
  • Weight: 594.74 gr
  • ISBN-10: 0471123587
  • Publisher Date: 15 Nov 1996
  • Height: 238.75 mm
  • No of Pages: 304
  • Spine Width: 19.25 mm
  • Width: 161 mm


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