Deriving traditional reproductive regimes to explain subnational fertility differentials in Zambia

Includes abstract.Includes bibliographical references (p. 228-245).This thesis applies multivariate statistical techniques to six data sets to account for past and present-day features underlying ethnic fertility differentials in Zambia

Nontraditional Approaches to Statistical Classification: Some Perspectives on Lp-Norm Methods

The body of literature on classification method which estimate boundaries between the groups (classes) by optimizing a function of the L_{p}-norm distances of observations in each group from these boundaries, is maturing fast. The number of published research articles on this topic, especially on mathematical programming (MP) formulations and techniques for L_{p}-norm classification, is now sizable. This paper highlights historical developments that have defined the field, and looks ahead at challenges that may shape new research directions in the next decade. In the first part, the paper summarizes basic concepts and ideas, and briefly reviews past research. Throughout, an attempt is made to integrate a number of the most important L_{p}-norm methods proposed to date within a unified framework, emphasizing their conceptual differences and similarities, rather than focusing on mathematical detail. In the second part, the paper discusses several potential directions for future research in this area. The long-term prospects of L_{p}-norm classification (and discriminant) research may well hinge upon whether or not the channels of communication between on the one hand researchers active in L_{p}-norm classification, who tend to have their roots primarily in decision sciences, the management sciences, computer sciences and engineering, and on the other hand practitioners and researchers in the statistical classification community, will be improved. This paper offers potential reasons for the lack of communication between these groups, and suggests ways in which L_{p}-norm research may be strengthened from a statistical viewpoint. The results obtained in L_{p}-norm classification studies are clearly relevant and of importance to all researchers and practitioners active in classification and discrimination analysis. The paper also briefly discusses artificial neural networks, a promising nontraditional method for classification which has recently emerged, and suggests that it may be useful to explore hybrid classification methods that take advantage of the complementary strengths of different methods, e.g., neural network and L_{p}-norm methods

A Novel Method for Comparative Analysis of Retinal Specialization Traits from Topographic Maps

Abstract Vertebrates possess different types of retinal specializations that vary in number, size, shape, and position in the retina. This diversity in retinal configuration has been revealed through topographic maps, which show variations in neuron density across the retina. Although topographic maps of about 300 vertebrates are available, there is no method for characterizing retinal traits quantitatively. Our goal is to present a novel method to standardize information on the position of the retinal specializations and changes in retinal ganglion cell (RGC) density across the retina from published topographic maps. We measured the position of the retinal specialization using two Cartesian coordinates and the gradient in cell density by sampling ganglion cell density values along four axes (nasal, temporal, ventral, and dorsal). Using this information, along with the peak and lowest RGC densities, we conducted discriminant function analyses (DFAs) to establish if this method is sensitive to distinguish three common types of retinal specializations (fovea, area, and visual streak). The discrimination ability of the model was higher when considering terrestrial (78%–80% correct classification) and aquatic (77%–86% correct classification) species separately than together. Our method can be used in the future to test specific hypotheses on the differences in retinal morphology between retinal specializations and the association between retinal morphology and behavioral and ecological traits using comparative methods controlling for phylogenetic effects

Pattern recognition techniques applied to rust classification in steel structures

The life and performance of steel structure depends directly upon the steel surface preparation. The restoration of steel structure such as steel bridges, ships and storage tanks is due mainly to the use of manual surface inspection methods accompanied by surface preparation technologies. It requires a long project duration, high costs and hazardous practices for both worker and environment to complete surface restoration. The developments of surface preparation technologies make it essential to develop technologies that allows patch restore of corrode steel structure in practice. This thesis addresses the problem of classification of rust steel surfaces. Various Pattern recognition methods are studied for classifying less subjective steel surfaces from a time corrosion perspective. Our primary contribution is: with appropriate features from the steel surfaces, artificial neural network pattern recognition methods have the abilities to classify the less subjective rust steel surfaces reliably and be suitable for automation. The results provide important information about the classification methods for rust steel surface analysis

Fourier analysis of otolith banding patterns to discriminate among hatchery, tributary, and lake shore incubated sockeye salmon (Oncorhynchus nerka) juveniles in Tustumena Lake, Alaska

Thesis (M.S.) University of Alaska Fairbanks, 1995Otolith banding patterns formed during incubation were used to discriminate among hatchery and wild sockeye salmon (Oncorhynchus nerka) fry from Tustumena Lake, Alaska. Banding patterns were described by Fourier analysis of otolith luminance profiles. Amplitudes of individual Fourier harmonics were used as discriminant variables. Estimates of total correct classification of otoliths to hatchery or wild origin were as high as 83.1% using quadratic discriminant function analysis on 10 Fourier amplitudes. The maximum total classification rate estimate among hatchery and five wild groups was 45.7% using linear discriminant function analysis on 14 Fourier amplitudes. Although classification rates for any individual group of wild incubated fry never exceeded 64%, site specific information was evident for all groups because the probability of classifying an individual to its true incubation location was significantly greater than chance. Results indicate phenotypic differences in otolith microstructure amongst incubation sites separated by < 10 km.U.S. Fish and Wildlife Service, National Biological Surve

Neural Network Approach to the Prediction of Violence

A backpropagation neural network and discriminant analysis were compared for their efficacy in the prediction of violent behavior.Forty-eight predictor� ,variables including demographic data, criminal history, psychome'tric data, substance abuse history, and situational factors were collected from official records of male criminal offenders (N = 392) and used to predict the violent or nonviolent nature of the offense for which each subject was incarcerated. Both neural netwo,rk (NN) and discriminant analysis (DA) models showed statistically significant prediction accuracy of about 77% total hits on cross-validation. As decision thresholds for classification were made increasingly stringent, however, the NN models held their accuracy better than the DA models. The highest levels of accuracy were achieved for both NN and DA models with a collection of 17 variables that included demographic data (age, income, race, unskilled labor), c�riminal history (probation and parole status, previous violent arrests), psychometric data (MMPI scales 1, 3, 8, 0; IQ), situational factors (being married, living with a mate, irregular work history, supporting a family), and substance abuse (benzodiazepihes).Psycholog

Altered Antioxidant-Oxidant Status in the Aqueous Humor and Peripheral Blood of Patients with Retinitis Pigmentosa

Retinitis Pigmentosa is a common form of hereditary retinal degeneration constituting the largest Mendelian genetic cause of blindness in the developed world. It has been widely suggested that oxidative stress possibly contributes to its pathogenesis. We measured the levels of total antioxidant capacity, free nitrotyrosine, thiobarbituric acid reactive substances (TBARS) formation, extracellular superoxide dismutase (SOD3) activity, protein, metabolites of the nitric oxide/cyclic GMP pathway, heme oxygenase-I and inducible nitric oxide synthase expression in aqueous humor or/and peripheral blood from fifty-six patients with retinitis pigmentosa and sixty subjects without systemic or ocular oxidative stress-related disease. Multivariate analysis of covariance revealed that retinitis pigmentosa alters ocular antioxidant defence machinery and the redox status in blood. Patients with retinitis pigmentosa present low total antioxidant capacity including reduced SOD3 activity and protein concentration in aqueous humor. Patients also show reduced SOD3 activity, increased TBARS formation and upregulation of the nitric oxide/cyclic GMP pathway in peripheral blood. Together these findings confirmed the hypothesis that patients with retinitis pigmentosa present reduced ocular antioxidant status. Moreover, these patients show changes in some oxidative-nitrosative markers in the peripheral blood. Further studies are needed to clarify the relationship between these peripheral markers and retinitis pigmentosa