Thesis (M. Soc. Sc.)--University of Hong Kong, 1985.

This dissertation analyzes the effects of Latino immigration on the representation of Latinos and African-Americans on local school boards and attempts to explain under what conditions Latino immigrants provoke opposition among whites. I consider two measures of representation based on representative bureaucracy—the membership of Latinos and African-Americans on school boards and bias in the responsiveness of white school board members toward these two groups. Whites as the major racial group in the U.S. have been the subject of much intergroup relations research focusing on competition for scarce resources, perceived threat and group biases (e.g., Evans and Giles, 1986; Giles and Evans, 1985, 1986; Esses, Jackson and Armstrong, 1998), and I also focus on their racial behaviors as voters in school board elections and as school board members. I consider Latino immigration in this research because emerging evidence suggests that Latino immigration poses a growing threat to whites, leading them to shift their support from Latinos to a countervailing group, such as African-Americans (e.g., Meier and Stewart, 1991; Rocha, 2007). First, I examine whether Latino immigration into a community affects the support of white citizens for Latino or African-American membership on school boards. Second, I examine whether white school board members also are influenced by Latino immigration in their responsiveness to Latino and African-American parents. It is likely that the reactions of whites to Latino immigration are conditioned by their preexisting racial attitudes, so this dissertation also tests competing theories of community racial climate—group threat and group contact. I expect that racial tensions within a community should moderate the influence of Latino immigration on these two forms of Latino and African-American representation. Overall, this dissertation expands the study of representative bureaucracy by combining past research on community racial climates with conditions influencing minority representation. In addition to examining the determinants of passive representation, this dissertation links expectations of the racial behavior of white citizens with the behavior of white school board members by considering the possibility that school board members express “discriminatory intent” (Mendez and Grose, 2014) on non-policy related matters. A better understanding of the determinants of public officials’ personal biases should help to explain the targeting of substantive policy benefits to minorities, which is the focus of much other representative bureaucracy research. While I base my analysis of school board membership on inferences of white voter behavior from aggregate election results, I directly measure white school board member responsiveness using data gathered from a novel randomized field experiment and e-mail audit design. Representative bureaucracy researchers have called for more of this type of individual-level data to help explain minority advocacy (Bradbury and Kellough, 2011).

Clustering algorithms are a primary tool in data analysis, facilitating the discovery of groups and structure in unlabeled data. They are used in a wide variety of industries and applications. Despite their ubiquity, clustering algorithms have a flaw: they take an unacceptable amount of time to run as the number of data objects increases. The need to compensate for this flaw has led to the development of a large number of techniques intended to accelerate their performance. This need grows greater every day, as collections of unlabeled data grow larger and larger. How does one increase the speed of a clustering algorithm as the number of data objects increases and at the same time preserve the quality of the results? This question was studied using the Fuzzy c-means clustering algorithm as a baseline. Its performance was compared to the performance of four of its accelerated variants. Four key design principles of accelerated clustering algorithms were identified. Further study and exploration of these principles led to four new and unique contributions to the field of accelerated fuzzy clustering. The first was the identification of a statistical technique that can estimate the minimum amount of data needed to ensure a multinomial, proportional sample. This technique was adapted to work with accelerated clustering algorithms. The second was the development of a stopping criterion for incremental algorithms that minimizes the amount of data required, while maximizing quality. The third and fourth techniques were new ways of combining representative data objects. Five new accelerated algorithms were created to demonstrate the value of these contributions. One additional discovery made during the research was that the key design principles most often improve performance when applied in tandem. This discovery was applied during the creation of the new accelerated algorithms. Experiments show that the new algorithms improve speedup with minimal quality loss, are demonstrably better than related methods and occasionally are an improvement in both speedup and quality over the base algorithm.

published_or_final_version / Politics and Public Administration / Master / Master of Public Administration

published_or_final_version / Politics and Public Administration / Doctoral / Doctor of Philosophy

published_or_final_version / Law / Doctoral / Doctor of Philosophy

published_or_final_version / Public Administration / Master / Master of Public Administration

published_or_final_version / Urban Studies / Master / Master of Social Sciences

No description available.

The subject of this study is sales forecasting of pharmaceutical representative company. The aim of the project described in this document is to create the custom tailored sales forecasting software for pharmaceutical representative company. The document consists of three main parts. In the analytical part the document the literature analysis and overview of existing solutions is performed. Most widely used forecasting techniques, methods and their appliance possibilities are described and classified. Advantages and disadvantages of existing forecasting tools are exposed. In accordance with the performed analysis the type of the system and forecasting methods that should be implemented in the system were selected. Key features of the created software design are described in the design part of the document. The created software has been examined with the real data of a pharmaceutical representative company. Generated sales forecasts were compared with real sales indexes. Results of the performed research were summarized and conclusions for usage of the created system were drawn.