This book presents an organized and well-knit account of the theory, methodology, extensions, and major applications of the Expectation-Maximization (EM) algorithm. It includes applications in the standard statistical contexts such as regression, factor analysis, variance components estimation, repeated-measures designs, categorical data analysis, survival analysis and survey sampling.
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"...should be comprehensible to graduates with statistics as their major subject." (Quarterly of Applied Mathematics, Vol. LIX, No. 3, September 2001)
This book presents an organized and well-knit account of the theory, methodology, extensions, and major applications of the Expectation-Maximization (EM) algorithm. It includes applications in the standard statistical contexts such as regression, factor analysis, variance components estimation, repeated-measures designs, categorical data analysis, survival analysis and survey sampling.
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