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Activity Number:
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238
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309182 |
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Title:
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Structure and Sparsity in High-Dimensional Multivariate Analysis
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Author(s):
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Carlos Carvalho*+
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Companies:
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Duke University
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Address:
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832 Marilee Glen Ct, Durham, NC, 27705,
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Keywords:
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Gaussian Graphical Models ; Sparse Factor Models
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Abstract:
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As scientific problems grow in terms of both expanding parameter dimension and sample sizes, structure and sparsity become central concepts in practical data analysis and inference. By allowing complex high-dimensional problems to be modeled through low-dimensional underlying relationships, sparsity helps to simplify estimation, reduce computational burden and facilitate interpretation of large scale datasets. This talk addresses the issue of sparsity modeling primarily in the context of Gaussian graphical models and sparse factor models. To convey the main ideas of this work the talk will focus on the extension of conditional independence ideas from Gaussian graphical models to multivariate dynamic linear models. After presenting the development of this new class of models I will describe applications of such models in large financial time series and portfolio allocation problems.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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