Unlike many of the current statistical models focusing on highly skewed longitudinal data, we present a novel model accommodating a skewed error distribution, partial linear median regression function, nonparametric wavelet expansion, and serial observations on the same unit. Parameters are estimated via a semiparametric Bayesian procedure using an appropriate Dirichlet process mixture prior for the skewed error distribution. We use a hierarchical mixture model as the prior for the wavelet coefficients. For the "vanishing" coefficients, the model includes a level dependent prior probability mass at zero. This practice implements wavelet coefficient thresholding as a Bayesian Rule. Practical advantages of our method are illustrated through a simulation study and via analysis of a cardiotoxicity study of children of HIV infected mother. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Summer Semester 2017. / May 23, 2017. / Bayesian, Longitudinal, Semiparametric, Wavelet / Includes bibliographical references. / Eric Chicken, Professor Co-Directing Dissertation; Debajyoti Sinha, Professor Co-Directing Dissertation; Kristine Harper, University Representative; Debdeep Pati, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_552030 |
| Contributors | Baker, Danisha S. (Danisha Sharice) (authoraut), Chicken, Eric, 1963- (professor co-directing dissertation), Sinha, Debajyoti (professor co-directing dissertation), Harper, Kristine (university representative), Pati, Debdeep (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Statistics (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (79 pages), computer, application/pdf |
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