Wavelet methods possess versatile properties for statistical applications. We would
like to explore the advantages of using wavelets in the analyses in two different research
areas. First of all, we develop an integrated tool for online detection of network
anomalies. We consider statistical change point detection algorithms, for both local
changes in the variance and for jumps detection, and propose modified versions of
these algorithms based on moving window techniques. We investigate performances
on simulated data and on network traffic data with several superimposed attacks. All
detection methods are based on wavelet packets transformations.
We also propose a Bayesian model for the analysis of high-throughput data where
the outcome of interest has a natural ordering. The method provides a unified approach
for identifying relevant markers and predicting class memberships. This is
accomplished by building a stochastic search variable selection method into an ordinal
model. We apply the methodology to the analysis of proteomic studies in prostate
cancer. We explore wavelet-based techniques to remove noise from the protein mass
spectra. The goal is to identify protein markers associated with prostate-specific antigen
(PSA) level, an ordinal diagnostic measure currently used to stratify patients into different risk groups.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2654 |
| Date | 01 November 2005 |
| Creators | Kwon, Deukwoo |
| Contributors | Vannucci, Marina |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 476388 bytes, electronic, application/pdf, born digital |
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