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Activity Number:
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59
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Type:
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Invited
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Date/Time:
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Sunday, August 8, 2004 : 6:00 PM to 7:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302044 |
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Title:
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Bayesian Methods for Analyzing Speech Recognition Scores
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Author(s):
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Matthew J. Hayat*+ and Prakash Laud
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Companies:
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Emmes Corporation and Medical College of Wisconsin
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Address:
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NIDCD, 6120 Executive Blvd., MSC 7180, Bethesda, MD, 20892,
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Keywords:
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Bayesian ; longitudinal ; serial correlation ; heterogeneity of variance ; modeling dependence ; Cholesky
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Abstract:
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Heterogeneity of variance and serial correlation are often present in measurements taken over time. The most popular parametric dependence models for serial correlation are stationary autoregressive models and other second-order stationary models. In these models, it is assumed that variances are constant over time and correlations between measurements equidistant in time are equal. These assumptions may not be reasonable. Our work considers a class of nonstationary models that allows for heterogeneity of variance and serial correlation. Modeling dependence is difficult for two reasons. First, dimensionality of the problem can be large in many applications and second, the covariance matrix must be constrained to be positive definite. A modified Cholesky decomposition of the precision matrix (inverse of the covariance matrix) allows us to address both of these challenges. It also produces nonstationary analogues of many stationary covariances with special structure that are available in the literature of longitudinal data analysis. We implement full Bayesian inference for several such models. Markov chain Monte Carlo techniques are used.
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