The EM Algorithm and Related Statistical Models (eBook, PDF)

Redaktion: Watanabe, Michiko; Yamaguchi, Kazunori

• Format: PDF

Produktbeschreibung

Exploring the application and formulation of the EM algorithm, The EM Algorithm and Related Statistical Models offers a valuable method for constructing statistical models when only incomplete information is available, and proposes specific estimation algorithms for solutions to incomplete data problems.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 250
• Erscheinungstermin: 15. Oktober 2003
• Englisch
• ISBN-13: 9780203913055
• Artikelnr.: 38181632

Autorenporträt

Michiko Watanabe, Kazunori Yamaguchi

Inhaltsangabe

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.