Measurement of target single-spin asymmetry in charged kaon electroproduction on a transversely polarized 3He target

The subject of quark transverse spin and transverse momentum distribution are two current research frontier in understanding the spin structure of the nucleons. The goal of the research reported in this dissertation is to extract new information on the quark transversity distribution and the novel transverse-momentum-dependent Sivers function in the neutron. A semi-inclusive deep inelastic scattering experiment was performed at the Hall A of the Jefferson laboratory using 5.9 GeV electron beam and a transversely polarized ^{3}He target. The scattered electrons and the produced hadrons (pions, kaons, and protons) were detected in coincidence with two large magnetic spectrometers. By regularly flipping the spin direction of the transversely polarized target, the single-spin-asymmetry (SSA) of the semi-inclusive deep inelastic reaction ^{3}He^{uparrow}(e,e'h^{\pm})X was measured over the kinematic range 0.13 < x < 0.41 and 1.3 < Q^{2} < 3.1 (GeV)^{2}. The SSA contains several different azimuthal angular modulations which are convolutions of quarks distribution functions in the nucleons and the quark fragmentation functions into hadrons. It is from the extraction of the various ``moments'' of these azimuthal angular distributions (Collins moment and Sivers moment) that we obtain information on the quark transversity distribution and the novel T-odd Sivers function. In this dissertation, I first introduced the theoretical background and experimental status of nucleon spins and the physics of SSA. I will then present the experimental setup and data collection of the JLab E06-010 experiment. Details of data analysis will be discussed next with emphasis on the kaon particle identification and the Ring-Imaging Cherenkov detector which are my major responsibilities in this experiment. Finally, results on the kaon Collins and Sivers moments extracted from the Maximum Likelihood method will be presented and interpreted. I will conclude with a discussion on the future prospects for this research.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.