Title page for etd-0019116-212723


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URN etd-0019116-212723
Author Ssu-ying Chang
Author's Email Address No Public.
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Department Applied Mathematics
Year 2015
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Brain Image Analysis of Patients with Depression and
Non-normal Mixed-Effects Models
Date of Defense 2015-06-30
Page Count 43
Keyword
  • skew t distribution
  • SVM
  • ROAD
  • wavelet
  • MMD
  • Abstract The aim of the first part of this study is to use binding potential (BP) image (and/or
    other covariates,such as aggression) to differentiate major mental disorders (MMD) patients from normal controls. We propose two methods to classify these two groups. First, we transform the BP image to the wavelet domain to reduce noise; Second, apply Regularized Optimal Affine Discriminant (method 1) and support vector machine (method 2) to the obtained wavelet coefficients to classify the two groups. We compare the misclassification rates, sensitivities, and specificities for these two methods with L1 penalty. In addition, we transform back the wavelet coefficients important to classification to the original image domain; these coefficient images would help us to understand with which brain regions the MMD is associated.
    In the second part, we bring up a new nonlinear and non-normal mixed-effects model,
    applications of this model can be quite extensive. we use this model to fit CA125 of ovarian cancer which are changing with time as an example. This model can be used to fit data in conjunction with many skewed distributions, and skew t distribution is just a distribution which we have chosen. Its related properties will be presented at the last half of this thesis.
    Advisory Committee
  • Mong-Na Lo Huang - chair
  • Fu-Chuen Chang - co-chair
  • An-Jen Chiang - co-chair
  • Mei-Hui Guo - co-chair
  • Chung Chang - advisor
  • Files
  • etd-0019116-212723.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2016-01-20

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