||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.