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Abstract Details
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Activity Number:
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191
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Consulting
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| Abstract - #304055 |
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Title:
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The Estimation and Likelihood Ratio Test for Non-Separable Covariance Matrices
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Author(s):
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Ranye Sun*+ and Mohsen Pourahmadi
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Companies:
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Texas A&M University and Texas A&M University
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Address:
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401 Anderson Street, College Station, TX, 77840, United States
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Keywords:
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Kronecker product ;
EM algorithm ;
Likelihood ratio test ;
Linear mixed models
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Abstract:
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We consider high-dimensional multivariate data with non-separable but additive covariance matrix. Estimation of the new model within the linear mixed model framework is considered, the estimators are computed using the EM algorithm and three different approximate likelihoods (e.g. quasi-likelihood, Newton-Raphson). We focus on the computational cost and estimation accuracy. The likelihood ratio test for additive non-separability against more general alternatives on the basis of a random sample from a multivariate normal population is studied. Some simulations and real data examples illustrate the methodology.
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