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Activity Number: 136
Type: Contributed
Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #307899
Title: A Fiducial Approach to Sparse Covariance Estimation
Author(s): Wen Shi*+ and Jan Hannig
Companies: UNC and UNC-Chapel Hill
Keywords: Covariance estimation ; sparse ; fiducial inference ; reversible jump Markov chain ; high dimension low sample size
Abstract:

We propose a sparse covariance estimation method for over-parametrized models via fiducial distribution of the covariate matrix with minimum description length penalty. Incorporating a reversible jump Markov chain Monte Carlo procedure, our fiducial distribution based approach efficiently produces a plausible covariance estimator as well as a confidence interval. The algorithm generates comparable results to existing methods with appropriate sparsity assumptions under the framework of high dimension low sample size.


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