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More Geoffrey J. McLachlan, Thriyambakam Krishnan "The EM Algorithm and Extensions"

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¡ªwhere data are missing, distributions are truncated, or observations are censored or grouped¡ªand 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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