The EM algorithm and extensions / Geoffrey J. McLachlan, Thriyambakam Krishnan.Material type: TextLanguage: English Series: Wiley series in probability and statisticsPublication details: Hoboken, N.J. : Wiley-Interscience, c2008. Edition: 2nd edDescription: xxvii, 359 p. : ill. ; 24 cmISBN: 9780471201700 (cloth); 0471201707 (cloth)Subject(s): Expectation-maximization algorithms | Estimation theory | Missing observations (Statistics)DDC classification: 519.544 MCE LOC classification: QA276.8 | .M394 2008Online resources: Table of contents only | Contributor biographical information | Publisher description | OCLC | E-book Fulltext
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|519.542 THB 2014 Bayesian analysis with Stata /||519.544 EFJ 1982 The jackknife, the bootstrap, and other resampling plans /||519.544 EGM 2009 Maximum penalized likelihood estimation /||519.544 MCE 2008 The EM algorithm and extensions /||519.544 MCE 2008 The EM algorithm and extensions /||519.544 TSS 2006 Semiparametric theory and missing data /||519.544 ZIG 2011 Generalized estimating equations /|
McLachlan, Geoffrey J., 1946-
EM algorithm and extensions.
Hoboken, N.J. : Wiley-Interscience, c2008
Includes bibliographical references (p. 311-337) and indexes.
Examples of the EM algorithm --
Basic theory of the EM algorithm --
Standard errors and speeding up convergence --
Extensions of the EM algorithm --
Monte Carlo versions of the EM algorithm --
Some generalizations of the EM algorithm --
Further 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." "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."--BOOK JACKET.