| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | May 31, 2005 |
| Latest Amendment Date: | May 31, 2005 |
| Award Number: | 0505490 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Gabor Szekely
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 1, 2005 |
| End Date: | July 31, 2008 (Estimated) |
| Total Intended Award Amount: | $83,998.00 |
| Total Awarded Amount to Date: | $83,998.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 NORTH AVE NW ATLANTA GA US 30332-0002 |
| 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: |
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| 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
This project deals with statistical inference in
phenomena that result in massive, multidimensional and/or
functional data sets. Prime examples are geophysical, biomedical,
and internet related data. In addition to high dimensionality, such
data are often characterized by self-affinity and require
non-standard (functional) models for their modeling and subsequent
statistical analysis. The methodology to be developed will advance
both the theory and practice of functional data analysis, a very
fast-developing and modern area of statistics. The common and novel
features of the statistical methods proposed here lie in the nature
of analyzed data. The data sets are massive, multidimensional,
functional, and possibly self-affine (fractal or multifractal).
Recent progress in multiscale data representations provide natural
and efficient environments for (i) developing scale-sensitive
analyzing tools for estimation, testing, classification, and
deconvolution, and (ii) describing, summarizing, and modeling
self-similar data. Bayesian methodology will be used whenever
available prior information can be incorporated or whenever sensible
automatic priors are possible.
Development of new inferential methodologies is critical for the
statistical support of recent scientific initiatives and newly
emerging technologies. The proposed research is application driven,
so the specificities of the application fields influence the design
and focus of the methodology. Techniques suggested in the proposal
deal with problems of testing of efficiency of new medical
treatments, target detection and classification as well as
classification of medical images, or more accurate recovery of radar
or satellite data. Hence, the methodologies which result from the
proposal are applicable in such areas of strategic interest as
health and medicine and homeland security. In addition to
methodological impact, the proposed research has a strong
educational component consisting of training graduate students,
involving undergraduate students in research projects, conducting
inter-departmental seminars, increasing awareness of mathematics
education among the work force, and attracting minority and female
students.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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