Multiscale Summaries of Probability Measures with Applications to Plant and Microbiome Data
Traditional descriptors such as the mean and the covariance matrix give useful global summaries of data and probability measures. Nevertheless, when distributions with more complex topological and geometrical behaviors arise, these methods fall short in accurately describing...
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| Format: | Others |
| Language: | English English |
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Florida State University
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| Online Access: | http://purl.flvc.org/fsu/fd/FSU_2016SP_DiazMartinez_fsu_0071E_13067 |
| Summary: | Traditional descriptors such as the mean and the covariance matrix give useful global summaries of data and probability
measures. Nevertheless, when distributions with more complex topological and geometrical behaviors arise, these methods fall short in
accurately describing them. This dissertation explores and develops new methods that provide more informative summaries of complex
probability measures using multiscale analogs of the Fréchet function and the covariance tensor which encode variation of data with
respect to any point in the domain. These multiscale methods are developed using kernel functions and diffusion distances and are helpful
in obtaining more information on local-to-regional-to-global behavior of probability measures, unlike the traditional take which only
gives global summaries. We applied the methods to the analysis of climatic data of the Fabaceae plant family (legumes) and to microbiome
data related to the Clostridium difficile infection in the human gut. Our studies reveal patterns of climatological adaptation of various
legume taxa and changes in the interactions of microbial communities in the presence of infection which are helpful in monitoring the
resolution of the disease. === A Dissertation submitted to the Department of Mathematics in partial fulfillment of the requirements
for the degree of Doctor of Philosophy. === Spring Semester 2016. === March 17, 2016. === climate-plant data, data analysis, microbiome data, multiscale descriptors, probability, statistics === Includes bibliographical references. === Washington Mio, Professor Directing Dissertation; Walter Tschinkel, University Representative;
Richard Bertram, Committee Member; Mike Mesterton-Gibbons, Committee Member. |
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