Call number: LD2668 .T4 1985 G875 / Master of Science

Call number: LD2668 .T4 1985 J54 / Master of Science

Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra. This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each contribute a fragment to understanding the graph; however, their combination allows us to understand dynamic behavior and graph structure. Spectral partitioning methods rely on eigenvectors for solving data analysis problems such as clustering. Eigenvectors of large sparse systems must be approximated with iterative methods. This dissertation analyzes how data analysis accuracy depends on the numerical accuracy of the eigensolver. This leads to new bounds on the residual tolerance necessary to guarantee correct partitioning. We present a novel stopping criterion for spectral partitioning guaranteed to satisfy the Cheeger inequality along with an empirical study of the performance on real world networks such as web, social, and e-commerce networks. This work bridges the gap between numerical analysis and computational data analysis.

A nitrogen-cooled and an argon-cooled hydrogen flame have been used for the determination of sulfur containing species in solids by molecular emission cavity analysis (MECA). The argon-cooled flame is much more sensitive for the determination of SO₄²⁻. In a solid mixture containing S₈, S²⁻, SO₃²⁻, and SO₄²⁻, the presence of one or more of these sulfur containing species can be determined with the argon-cooled flame. The nitrogen-cooled flame can be useful, for example, in the determination of a mixture of S₈ and SO₃²⁻ in a solid matrix. All these sulfur containing species can be quantitatively determined in the argon-cooled flame in the concentration range from about 10 ppm to 5000 ppm. The variation from 10 percent to 30 percent in the reproducibility of these measurements has been attributed to the non-homogeneity of the solid materials and the small sizes required. Sulfur dioxide has been used for the reduction of ammoniacal copper(II) solutions to solutions containing various copper(I) compounds. These copper(I) compounds can be reduced further to copper metal by varying the solution conditions. The mechanisms of the reactions involved must be understood before they can be successfully used for the large scale production of copper. Porth et al.'s method was followed for the synthesis of Cu(I) intermediates. Several compounds were isolated and their compositions determined. The changes in the relative concentrations of Cu(I) and Cu(II) are also important for unraveling the kinetics and mechanisms of these reactions. A simple spectrophotometric method using 2,9-dimethyl-1,10-phenanthroline was developed to monitor the Cu(I) concentration in solution. The sensitivity of the method is sufficient to determine 10⁻⁵ M Cu(I) in the presence of Cu(II); SO₂, however, interferes with the method. Other possible methods including the use of a mixture of EDTA and 2,9-dimethyl-1,10-phenanthroline were also examined. Evidence is presented for the formation of a ternary complex of copper(I), 2-9-dimethyl-1,10-phenanthroline, and EDTA. The possibility of using a mixture of Cu(II) and 2,9-dimethyl-1,10-phenanthroline to determine SO₂ was tested. Oxygen was found to interfere with this method.

Thesis (PhD)--Stellenbosch University, 1997. / One copy microfiche. / ENGLISH ABSTRACT: Discriminani analysis and logistic regression are techniques that can be used to classify entities of unknown origin into one of a number of groups. However, the underlying models and assumptions for application of the two techniques differ. In this study, the two techniques are compared with respect to classification of entities. Firstly, the two techniques were compared in situations where no data dependent variable selection took place. Several underlying distributions were studied: the normal distribution, the double exponential distribution and the lognormal distribution. The number of variables, sample sizes from the different groups and the correlation structure between the variables were varied to' obtain a large number of different configurations. .The cases of two and three groups were studied. The most important conclusions are: "for normal and double' exponential data linear discriminant analysis outperforms logistic regression, especially in cases where the ratio of the number of variables to the total sample size is large. For lognormal data, logistic regression should be preferred, except in cases where the ratio of the number of variables to the total sample size is large. " Variable selection is frequently the first step in statistical analyses. A large number of potenti8.Ily important variables are observed, and an optimal subset has to be selected for use in further analyses. Despite the fact that variable selection is often used, the influence of a selection step on further analyses of the same data, is often completely ignored. An important aim of this study was to develop new selection techniques for use in discriminant analysis and logistic regression. New estimators of the postselection error rate were also developed. A new selection technique, cross model validation (CMV) that can be applied both in discriminant analysis and logistic regression, was developed. ."This technique combines the selection of variables and the estimation of the post-selection error rate. It provides a method to determine the optimal model dimension, to select the variables for the final model and to estimate the post-selection error rate of the discriminant rule. An extensive Monte Carlo simulation study comparing the CMV technique to existing procedures in the literature, was undertaken. In general, this technique outperformed the other methods, especially with respect to the accuracy of estimating the post-selection error rate. Finally, pre-test type variable selection was considered. A pre-test estimation procedure was adapted for use as selection technique in linear discriminant analysis. In a simulation study, this technique was compared to CMV, and was found to perform well, especially with respect to correct selection. However, this technique is only valid for uncorrelated normal variables, and its applicability is therefore limited. A numerically intensive approach was used throughout the study, since the problems that were investigated are not amenable to an analytical approach. / AFRIKAANSE OPSOMMING: Lineere diskriminantanaliseen logistiese regressie is tegnieke wat gebruik kan word vir die Idassifikasie van items van onbekende oorsprong in een van 'n aantal groepe. Die agterliggende modelle en aannames vir die gebruik van die twee tegnieke is egter verskillend. In die studie is die twee tegnieke vergelyk ten opsigte van k1assifikasievan items. Eerstens is die twee tegnieke vergelyk in 'n apset waar daar geen data-afhanklike seleksie van veranderlikes plaasvind me. Verskeie onderliggende verdelings is bestudeer: die normaalverdeling, die dubbeleksponensiaal-verdeling,en die lognormaal verdeling. Die aantal veranderlikes, steekproefgroottes uit die onderskeie groepe en die korrelasiestruktuur tussen die veranderlikes is gevarieer om 'n groot aantal konfigurasies te verkry. Die geval van twee en drie groepe is bestudeer. Die belangrikste gevolgtrekkings wat op grond van die studie gemaak kan word is: vir normaal en dubbeleksponensiaal data vaar lineere diskriminantanalise beter as logistiese regressie, veral in gevalle waar die. verhouding van die aantal veranderlikes tot die totale steekproefgrootte groot is. In die geval van data uit 'n lognormaalverdeling, hehoort logistiese regressie die metode van keuse te wees, tensy die verhouding van die aantal veranderlikes tot die totale steekproefgrootte groot is. Veranderlike seleksie is dikwels die eerste stap in statistiese ontledings. 'n Groot aantal potensieel belangrike veranderlikes word waargeneem, en 'n subversamelingwat optimaal is, word gekies om in die verdere ontledings te gebruik. Ten spyte van die feit dat veranderlike seleksie dikwels gebruik word, word die invloed wat 'n seleksie-stap op verdere ontledings van dieselfde data. het, dikwels heeltemal geYgnoreer.'n Belangrike doelwit van die studie was om nuwe seleksietegniekete ontwikkel wat gebruik kan word in diskriminantanalise en logistiese regressie. Verder is ook aandag gegee aan ontwikkeling van beramers van die foutkoers van 'n diskriminantfunksie wat met geselekteerde veranderlikes gevorm word. 'n Nuwe seleksietegniek, kruis-model validasie (KMV) wat gebruik kan word vir die seleksie van veranderlikes in beide diskriminantanalise en logistiese regressie is ontwikkel. Hierdie tegniek hanteer die seleksie van veranderlikes en die beraming van die na-seleksie foutkoers in een stap, en verskaf 'n metode om die optimale modeldimensiete bepaal, die veranderlikes wat in die model bevat moet word te kies, en ook die na-seleksie foutkoers van die diskriminantfunksie te beraam. 'n Uitgebreide simulasiestudie waarin die voorgestelde KMV-tegniek met ander prosedures in die Iiteratuur. vergelyk is, is vir beide diskriminantanaliseen logistiese regressie ondemeem. In die algemeen het hierdie tegniek beter gevaar as die ander metodes wat beskou is, veral ten opsigte van die akkuraatheid waarmee die na-seleksie foutkoers beraam word. Ten slotte is daar ook aandag gegee aan voor-toets tipeseleksie. 'n Tegniek is ontwikkel wat gebruik maak van 'nvoor-toets berarningsmetode om veranderlikes vir insluiting in 'n lineere diskriminantfunksie te selekteer. Die tegniek ISin 'n simulasiestudie met die KMV-tegniek vergelyk, en vaar baie goed, veral t.o.v. korrekte seleksie. Hierdie tegniek is egter slegs geldig vir ongekorreleerde normaalveranderlikes, wat die gebruik darvan beperk. 'n Numeries intensiewe benadering is deurgaans in die studie gebruik. Dit is genoodsaak deur die feit dat die probleme wat ondersoek is, nie deur middel van 'n analitiese benadering hanteer kan word nie.

published_or_final_version / Chemistry / Doctoral / Doctor of Philosophy

Differential item functioning (DIF) detection rates were examined for the logistic regression and analysis of variance (ANOVA) DIF detection methods. The methods were applied to simulated data sets of varying test length (20, 40, and 60 items) and sample size (200, 400, and 600 examinees) for both equal and unequal underlying ability between groups as well as for both fixed and varying item discrimination parameters. Each test contained 5% uniform DIF items, 5% non-uniform DIF items, and 5% combination DIF (simultaneous uniform and non-uniform DIF) items. The factors were completely crossed, and each experiment was replicated 100 times. For both methods and all DIF types, a test length of 20 was sufficient for satisfactory DIF detection. The detection rate increased significantly with sample size for each method. With the ANOVA DIF method and uniform DIF, there was a difference in detection rates between discrimination parameter types, which favored varying discrimination and decreased with increased sample size. The detection rate of non-uniform DIF using the ANOVA DIF method was higher with fixed discrimination parameters than with varying discrimination parameters when relative underlying ability was unequal. In the combination DIF case, there was a three-way interaction among the experimental factors discrimination type, relative ability, and sample size for both detection methods. The error rate for the ANOVA DIF detection method decreased as test length increased and increased as sample size increased. For both methods, the error rate was slightly higher with varying discrimination parameters than with fixed. For logistic regression, the error rate increased with sample size when relative underlying ability was unequal between groups. The logistic regression method detected uniform and non-uniform DIF at a higher rate than the ANOVA DIF method. Because the type of DIF present in real data is rarely known, the logistic regression method is recommended for most cases.

This study contributes to the debate on whether fundamental analysis and technical analysis techniques can be used jointly in making investment decisions. Extant literature on fundamental and technical analysis techniques has frequently focused on analysing each of the valuation techniques independently of one another. In this study we construct a model that integrates both fundamental and technical analysis variables (hybrid model) to determine whether the hybrid model can have a superior explanatory power to models based on each of the valuation techniques in isolation. This study is based on all ordinary shares that have been listed on the JSE main board between the 2002 and 2012 fiscal years. Testing rejects the complimentary nature of fundamental and technical analysis techniques by showing that the technical analysis model has a superior explanatory power to both the hybrid model and the fundamental analysis model. We also demonstrate that JSE-listed stocks do not exhibit momentum or contrarian effects with respect to return performances and that the fundamental analysis variables that play a significant role in explaining stock price movements of JSE-listed stocks are the book value per share, cash flow per share, earnings per share and dividends per share.

by Ho, Wai Fun. / Thesis (M.Sc.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 79-87). / LIST OF TABLES --- p.iv / LIST OF FIGURES --- p.vi / ACKNOWLEDGEMENTS --- p.vii / ABSTRACT --- p.viii / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- METABOLIC DERANGEMENTS AND CACHEXIA IN CANCER --- p.1 / Chapter 1.2 --- PROTEIN METABOLISM IN MALIGNANCY --- p.4 / Chapter 1.3 --- REVIEW OF REPORTS ON AMINO ACID DISTURBANCES IN MALIGNANCY --- p.5 / Chapter 1.4 --- AMINO ACID ANALYSIS BY HIGH PERFORMANCE LIQUID CHROMATOGRAPHY --- p.10 / Chapter 1.4.1 --- Amino Acid Analysis by Ion-Exchange HPLC --- p.11 / Chapter 1.4.2 --- Amino Acid Analysis by Reversed-Phase HPLC --- p.13 / Chapter 1.4.3 --- Derivatizing Agents --- p.15 / Chapter 1.5 --- CHOICE OF CANCER PATIENTS AND METHODOLOGY FOR THIS STUDY --- p.19 / Chapter 1.5.1 --- Choice of Cancer Patients --- p.19 / Chapter 1.5.2 --- Methodology Chosen and Its Principle --- p.20 / Chapter 2. --- OBJECTIVES --- p.23 / Chapter 3. --- MATERIALS AND METHODS --- p.24 / Chapter 3.1 --- STUDY SUBJECTS --- p.24 / Chapter 3.1.1 --- Patients --- p.24 / Chapter 3.1.2 --- Control Subjects --- p.25 / Chapter 3.2 --- CLINICAL FEATURES --- p.25 / Chapter 3.3 --- BLOOD COLLECTION --- p.25 / Chapter 3.4 --- GENERAL BIOCHEMICAL TESTS --- p.26 / Chapter 3.5 --- PLASMA AMINO ACID ANALYSIS BY HPLC --- p.26 / Chapter 3.5.1 --- Apparatus --- p.26 / Chapter 3.5.2 --- Reagents --- p.27 / Chapter 3.5.3 --- Reagent Preparation --- p.28 / Chapter 3.5.3.1 --- Mobile phase --- p.28 / Chapter 3.5.3.2 --- Derivatizing reagent --- p.29 / Chapter 3.5.4 --- Standard Preparation --- p.29 / Chapter 3.5.4.1 --- Internal standard solution --- p.29 / Chapter 3.5.4.2 --- Composite standard solution --- p.30 / Chapter 3.5.4.3 --- Composite standard-internal standard mixture --- p.32 / Chapter 3.5.5 --- Sample Preparation --- p.32 / Chapter 3.5.5.1 --- Protein removal --- p.32 / Chapter 3.5.5.2 --- Addition of internal standard --- p.32 / Chapter 3.5.6 --- Preparation of Samples for the WISP Sample Processor --- p.33 / Chapter 3.5.7 --- Sample Queue for the WISP Sample Processor --- p.33 / Chapter 3.5.8 --- Automated Derivatization Procedure --- p.36 / Chapter 3.5.9 --- Chromatographic Conditions --- p.36 / Chapter 3.6 --- STATISTICAL STUDIES --- p.38 / Chapter 4. --- RESULTS --- p.40 / Chapter 4.1 --- ANALYTICAL PERFORMANCES --- p.40 / Chapter 4.1.1 --- Chromatograms --- p.40 / Chapter 4.1.2 --- Precision --- p.47 / Chapter 4.1.3 --- Linearity --- p.47 / Chapter 4.1.4 --- Analytical Recovery --- p.51 / Chapter 4.2 --- DATA DISTRIBUTION STUDIES --- p.53 / Chapter 4.3 --- PATIENTS' ANTHROPOMETRIC DATA AND BLOOD BIOCHEMISTRY --- p.55 / Chapter 4.4 --- FREE PLASMA AMINO ACID CONCENTRATIONS IN NORMAL CONTROLS --- p.59 / Chapter 4.5 --- FREE PLASMA AMINO ACID CONCENTRATIONS IN CANCER PATIENTS --- p.59 / Chapter 5. --- DISCUSSION --- p.68 / Chapter 5.1 --- METHOD ESTABLISHMENT --- p.68 / Chapter 5.2 --- NORMAL CONTROLS --- p.68 / Chapter 5.3 --- CANCER PATIENTS --- p.71 / Chapter 5.3.1 --- Nasopharyngeal Cancer --- p.71 / Chapter 5.3.2 --- Lung Cancer --- p.71 / Chapter 5.3.3 --- Breast Cancer --- p.72 / Chapter 5.3.4 --- Colorectal Cancer --- p.74 / Chapter 5.4 --- SUMMARY OF THE PLASMA AMINO ACID PROFILES IN CANCER --- p.75 / Chapter 5.5 --- FURTHER STUDIES --- p.76 / Chapter 6. --- CONCLUSION --- p.78 / Chapter 7. --- REFERENCES --- p.79

by Yuen Wai-ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 50-51). / Abstract also in Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Estimation of Factor Scores in a Three-level Factor Analysis Model / Chapter 2.1 --- The Three-level Factor Analysis Model --- p.5 / Chapter 2.2 --- Estimation of Factor Scores in Between-group --- p.7 / Chapter 2.2.1 --- REG Method --- p.9 / Chapter 2.2.2 --- GLS Method --- p.11 / Chapter 2.3 --- Estimation of Factor Scores in Second Level Within-group --- p.13 / Chapter 2.3.1 --- REG Method --- p.15 / Chapter 2.3.2 --- GLS Method --- p.17 / Chapter 2.4 --- Estimation of Factor Scores in First Level Within-group / Chapter 2.4.1 --- First Approach --- p.19 / Chapter 2.4.2 --- Second Approach --- p.24 / Chapter 2.4.3 --- Comparison of the Two Approaches in Estimating Factor Scores in First Level Within-group --- p.31 / Chapter 2.5 --- Summary on the REG and GLS Methods --- p.35 / Chapter Chapter 3 --- Simulation Studies / Example1 --- p.37 / Example2 --- p.42 / Chapter Chapter 4 --- Conclusion and Discussion --- p.48 / References --- p.50 / Figures --- p.52