The Em Algorithm and Related Statistical Models

ISBN-13: 9780824747015 / Angielski / Twarda / 2003 / 216 str.

Michiko Watanabe; Kazunori Yamaguchi; Watanabe Watanabe

Opis

This text provides a comprehensive description of a method of constructing a statistical model when only incomplete data is available. It then proposes specific estimation algorithms for solving various individual incomplete data problems. It discusses the realistic problems on the processing of missing values commonly seen in multidimensional data and the various methods to cope with them.

Spis treści

Incomplete data and the generation mechanisms; type of incomplete data and its analysis; statistical models for incomplete data; analysis of data with missing values; missing data in multinominal data; algorithms for MLE for multivariate normal datawith missing values; scoring method; EM algorithm; basics of EM algorithm; extension of EM algorithm and acceleration; EM algorithm as an optimization tool robust model and outlier detection; scale mixture model of normal distributions; multivariate andcontaminated normal distribution; robust tobit model; robust factor model; statistical model with latent variables; latent structure model and EM algorithm; latent class model and latent trait model; structured equations model with latent variables;extensions of EM algorithm; ECM algorithm; ECME algorithm; optimal EM algorithm; MCEM algorithm; covergence speed of EM algorithm; convergence speed; comparisons of EM and other optimization algorithms quasi Newton method; acceleration methods of the EMalgorithm; neural networks and EM algorithm; EM algorithm in neural networks; geometric interpretation of EM algorithm; Marcov chain Monte Carlo; Bayes estimation; Marcov chain; Metropolis-Hastings algorithm; data augmentation algorithm; poor man's dataaugmentation algorithm; Gibbs sampling algorithm. Appendices: SOLAS for missing data analysis; Lem.

O autorze

Michiko Watanabe, Kazunori Yamaguchi