| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | May 24, 2007 |
| Latest Amendment Date: | May 6, 2009 |
| Award Number: | 0705007 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Gabor Szekely
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 1, 2007 |
| End Date: | July 31, 2012 (Estimated) |
| Total Intended Award Amount: | $345,043.00 |
| Total Awarded Amount to Date: | $345,043.00 |
| Funds Obligated to Date: |
FY 2008 = $115,972.00 FY 2009 = $119,213.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 (650)723-2300 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
450 JANE STANFORD WAY STANFORD CA US 94305-2004 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | STATISTICS |
| Primary Program Source: |
01000809DB NSF RESEARCH & RELATED ACTIVIT 01000910DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
The investigator studies and develops computer-intensive methods for applied statistics, with applicatiion in biology. His current proposal has three projects a) complementary clustering, a kind of orthogonal decomposition analogous to principal compoennts, b) the fused lasso for spatial smoothing and hot-spot detection, c) pre-validation- a method for inference for the p>N setting and d) The new edition of the text "The Elements of Statistical Learning"
There have been significant developments in the areas of applied regression and classification over the past 10-15 years. Much of the impetus originally came from outside of the field of statistics, from areas such as computer science, machine learning and neural networks. As a result, we now have at our disposal a very powerful collection of techniques for adaptive regression and classification. These are now being applied to medical diagnosis, bioinformatics and genetic modeling, chemical process control, shape, handwriting, speech and face recognition, financial modeling, and a wide range of other important practical problems. In this work the investigator plans to develop and study new tools for the important practical problems.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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