JSM 2005 - Toronto

Abstract #302518

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 425
Type: Invited
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #302518
Title: EM as a Unifying Approach for Incomplete Data Structures
Author(s): Robert Shumway*+
Companies: University of California, Davis
Address: Department of Statistics, Davis, CA, 95616, United States
Keywords: EM ; Imputation ; Censored Data ; Missing Data ; State Space Model
Abstract:

In this paper, we recount three success stories involving application of the EM algorithm to incompletely observed univariate and multivariate data with possible latent variables. As a first example, estimating seismic magnitudes and station corrections involves handling large-scale arrays with many missing and censored observations. By data augmentation and the EM algorithm, successive maximization steps can be reduced to simple operations involving row and column means. A second example involves imputing missing observations in multivariate pollution time series using a state space model. As a final example, EM approaches to several blind deconvolution problems, with the signal as an unobserved latent variable, are presented.

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Revised March 2005