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High-Dimensional Statistics
High-Dimensional Statistics has grown out of modern research activities in diverse fields such as science, technology, and business, aided by powerful computing
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High-Dimensional Statistics
It encompasses several emerging fields in statistics such as high-dimensional inference, dimension reduction, data mining, machine learning, and bioinformatics.
Conference On
High-Dimensional Statistics
High-dimensional statistical techniques are being integrated into courses in other disciplines to create advanced interdisciplinary programs.
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Sudipto Banerjee

Title of Talk: On hierarchical modeling for massively scalable inference from spatially oriented datasets

Abstract: Advances in geospatial technologies have created data-rich environments which provide extraordinary opportunities to understand the complexity of large and spatially indexed data and help tackle increasingly complex inferential questions in the natural sciences. A setting commonly encountered today is where a very large number of spatially-referenced outcomes have been monitored over different spatial locations and inferential interest resides with how the relationships among these outcomes across space. These lead to high-dimensional spatial process models that accommodate richness and flexibility, such as non-stationary behavior. However, direct application of such multivariate models to even moderate-sized spatial data, unfortunately, is computationally prohibitive. Here, we discuss approaches that help overcome these inferential hurdles without sacrificing richness in underlying association structures. We focus upon a few different approaches: (a) low-rank spatial processes, (b) conditionally specified low-dimensional approximations, and (c) spatial meta-analysis that pools independent spatial models across different computational cores to offer massively scalable inference.


Brief Bio:

Sudipto Banerjee’s research, dissertation advising and mentoring
activities focus upon statistical modeling and analysis of
geographically referenced datasets, Bayesian statistics, interface
between statistics and Geographical Information Systems, and statistical
computing. He has published over ninety peer-reviewed journal articles,
several book chapters and has co-authored a book titled “Hierarchical
Modeling and Analysis for Spatial Data”. Over half of these have emerged
directly from Dr. Banerjee’s mentoring and advising activities to
students seeking MS and PhD degrees in Biostatistics. In 2009 he was
honored with the Abdel El Sharaawi Award from the International
Environmetrics Society. In 2011 he was honored with the Mortimer
Spiegelman Award from the American Association of Public Health. Dr.
Banerjee is also an elected Fellow of the American Statistical
Association and an elected member of the International Statistical