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SAMSI Statistical Inference in Sparse High-dimensional Models: theoretical and computational challenges

Event Dates: Monday  February 24 - Wednesday  February 26
City: RTP   State: North Carolina   Country: USA
Event Type: Workshops & Seminars ASA Sponsored: No
This workshop focuses on both theoretical and computational developments in high-dimensional statistical models. Of particular interest are models that involve high-dimensional matrix estimation, such as elliptical copula models, graphical and network models, factor models, and functional data. These models are typically parametrized by matrices of reduced complexity, for instance of low rank, low effective rank, with sparse patterns, or some combination of these. The low-complexity assumptions are crucial for the successful implementation and theoretical analysis of such models, especially from a limited amount of data. High-dimensional models with low-dimensional structures are ubiquitous. Rich applications occur in genetics, neuroscience, economics, public health, psychology and sociology. New scientific challenges in these established areas, or in emerging areas such as medical geology or action science, arise on a continual basis, and with them the need to meet them at both computational and theoretical levels. This workshop will bring together researchers in applied, computational, and theoretical statistics, with the goals of (i) identifying pressing scientific open questions that can be answered within the framework of the workshop; (ii) disseminating state of the art results in the area of high dimensional statistical inference; and (iii) identifying open theoretical and computational challenges in this area.
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