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.

The effect of borders on the linguistic production and perception of regional identity in Louisville, Kentucky

A great deal of scholarly research has addressed the issue of dialect mapping in the United States. These studies, usually based on phonetic or lexical items, aim to present an overall picture of the dialect landscape. But what is often missing in these types of projects is an attention to the borders of a dialect region and to what kinds of identity alignments can be found in such areas. This lack of attention to regional and dialect border identities is surprising, given the salience of such borders for many Americans. This salience is also ignored among dialectologists, as nonlinguists‟ perceptions and attitudes have been generally assumed to be secondary to the analysis of “real” data, such as the phonetic and lexical variables used in traditional dialectology. Louisville, Kentucky is considered as a case study for examining how dialect and regional borders in the United States impact speakers‟ linguistic acts of identity, especially the production and perception of such identities. According to Labov, Ash, and Boberg (2006), Louisville is one of the northernmost cities to be classified as part of the South. Its location on the Ohio River, on the political and geographic border between Kentucky and Indiana, places Louisville on the isogloss between Southern and Midland dialects. Through an examination of language attitude surveys, mental maps, focus group interviews, and production data, I show that identity alignments in borderlands are neither simple nor straightforward. Identity at the border is fluid, complex, and dynamic; speakers constantly negotiate and contest their identities. The analysis shows the ways in which Louisvillians shift between Southern and non-Southern identities, in the active and agentive expression of their amplified awareness of belonging brought about by their position on the border.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.