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Financial Transfer and Its Impact on the Level of Democracy: A Pooled Cross-Sectional Time Series Model.

Description: This dissertation is a pooled time series, cross-sectional, quantitative study of the impact of international financial transfer on the level of democracy. The study covers 174 developed and developing countries from 1976 through 1994. Through evaluating the democracy and democratization literature and other studies, the dissertation develops a theory and testable hypotheses about the impact of the international variables foreign aid and foreign direct investment on levels of democracy. This study sought to determine whether these two financial variables promote or nurture democracy and if so, how? A pooled time-series cross-sectional model is developed employing these two variables along with other relevant control variables. Control variables included the presence of the Cold War and existence of formal alliance with the United States, which account for the strategic dimension that might affect the financial transfer - level of democracy linkage. The model also includes an economic development variable (per capita Gross National Product) to account for the powerful impact for economic development on the level of democracy, as well as a control for each country's population size. By addressing and the inclusion of financial, economic, strategic, and population size effects, I consider whether change in these variables affect the level of democracy and in which direction. The dissertation tests this model by employing several techniques. The variables are subjected to bivariate and multivariate analysis including bivariate correlations, analysis of variance, and ordinary least square (OLS) multivariate regression with robust matrix and a lagged dependent variable. Panel corrected standard error (PCSE) was also employed to empirically test the pooled timeseries cross-sectional multivariate model. The dissertation analytical section concludes with path analysis testing which showed the impact of each of the independent variables on the dependent variable. The findings indicate less impact of international financial variables upon the level of democracy than hypothesized. ...
Date: May 2003
Creator: Al-Momani, Mohammad H.
Partner: UNT Libraries

Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities

Description: The distributional properties of improvement-over-chance, I, effect sizes derived from linear and quadratic predictive discriminant analysis (PDA) and from logistic regression analysis (LRA) for the two-group univariate classification were examined. Data were generated under varying levels of four data conditions: population separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the decision of which method to choose is primarily determined by variance pattern and prior probabilities. Under variance homogeneity, any of the methods may be recommended. However, LRA is recommended when priors are equal or extreme and linear PDA is recommended when priors are moderate. Under variance heterogeneity, selecting a recommended method is more complex. In many cases, more than one method could be used appropriately.
Date: May 2003
Creator: Alexander, Erika D.
Partner: UNT Libraries

The Use of Genetic Polymorphisms and Discriminant Analysis in Evaluating Genetic Polymorphisms as a Predictor of Population

Description: Discriminant analysis is a procedure for identifying the relationships between qualitative criterion variables and quantitative predictor variables. Data bases of genetic polymorphisms are currently available that group such polymorphisms by ethnic origin or nationality. Such information could be useful to entities that base financial determinations upon predictions of disease or to medical researchers who wish to target prevention and treatment to population groups. While the use of genetic information to make such determinations is unlawful in states and confidentiality and privacy concerns abound, methods for human “redlining” may occur. Thus, it is necessary to investigate the efficacy of the relationship of certain genetic information to ethnicity to determine if a statistical analysis can provide information concerning such relationship. The use of the statistical technique of discriminant analysis provides a tool for examining such relationship.
Date: May 2002
Creator: Howell, Bruce F.
Partner: UNT Libraries

Mathematical Programming Approaches to the Three-Group Classification Problem

Description: In the last twelve years there has been considerable research interest in mathematical programming approaches to the statistical classification problem, primarily because they are not based on the assumptions of the parametric methods (Fisher's linear discriminant function, Smith's quadratic discriminant function) for optimality. This dissertation focuses on the development of mathematical programming models for the three-group classification problem and examines the computational efficiency and classificatory performance of proposed and existing models. The classificatory performance of these models is compared with that of Fisher's linear discriminant function and Smith's quadratic discriminant function. Additionally, this dissertation investigates theoretical characteristics of mathematical programming models for the classification problem with three or more groups. A computationally efficient model for the three-group classification problem is developed. This model minimizes directly the number of misclassifications in the training sample. Furthermore, the classificatory performance of the proposed model is enhanced by the introduction of a two-phase algorithm. The same algorithm can be used to improve the classificatory performance of any interval-based mathematical programming model for the classification problem with three or more groups. A modification to improve the computational efficiency of an existing model is also proposed. In addition, a multiple-group extension of a mathematical programming model for the two-group classification problem is introduced. A simulation study on classificatory performance reveals that the proposed models yield lower misclassification rates than Fisher's linear discriminant function and Smith's quadratic discriminant function under certain data configurations. Data configurations, where the parametric methods outperform the proposed models, are also identified. A number of theoretical characteristics of mathematical programming models for the classification problem are identified. These include conditions for the existence of feasible solutions, as well as conditions for the avoidance of degenerate solutions. Additionally, conditions are identified that guarantee the classificatory non-inferiority of one model over another in the training ...
Date: August 1993
Creator: Loucopoulos, Constantine
Partner: UNT Libraries

The Effect of Certain Modifications to Mathematical Programming Models for the Two-Group Classification Problem

Description: This research examines certain modifications of the mathematical programming models to improve their classificatory performance. These modifications involve the inclusion of second-order terms and secondary goals in mathematical programming models. A Monte Carlo simulation study is conducted to investigate the performance of two standard parametric models and various mathematical programming models, including the MSD (minimize sum of deviations) model, the MIP (mixed integer programming) model and the hybrid linear programming model.
Date: May 1994
Creator: Wanarat, Pradit
Partner: UNT Libraries

The Prediction of Industrial Bond Rating Changes: a Multiple Discriminant Model Versus a Statistical Decomposition Model

Description: The purpose of this study is to investigate the usefulness of statistical decomposition measures in the prediction of industrial bond rating changes. Further, the predictive ability of decomposition measures is compared with multiple discriminant analysis on the same sample. The problem of this study is twofold. It stems in general from the statistical problems associated with current techniques employed in the study of bond ratings and in particular from the lack of attention to the study of bond rating changes. Two main hypotheses are tested in this study. The first is that bond rating changes can be predicted through the use of financial statement data. The second is that decomposition analysis can achieve the same performance as multiple discriminant analysis in duplicating and predicting industrial bond rating changes. To explain and predict industrial bond rating changes, statistical decomposition measures were computed for each company in the sample. Based on these decomposition measures, the two types of analyses performed were (a) a univariate analysis where each decomposition measure was compared with an industry average decomposition measure, and (b) a multivariate analysis where decomposition measures were used as independent variables in a probability linear model. In addition to statistical decomposition analysis, multiple discriminant analysis was used in duplicating and predicting bond rating changes. Finally, a comparison was made between the predictive abilities of decomposition analysis and discriminant analysis.
Date: December 1985
Creator: Metawe, Saad Abdel-Hamid
Partner: UNT Libraries