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A combined analytical and empirical method to define human performance measures is required for effective automated training. In theory, performance measures can be weighted and combined in a single first order equation upon which an automated training system can track a student's comparative skill level. A form of multivariate discriminant analysis was the statistical technique incorporated into the measurement selection and weighting scheme. (Vreuls and Wooldridge 1977). As with regression analysis, this discriminant technique was subject to debilitating problems such as overfit and undesirable inaccuracies in the coefficients. The author developed and implemented an adjustment to the discriminant function analogous to the ridge regression analysis suggested by Hoerl and Kemmard (1970a). To further demonstrate the effectiveness of ridge discriminant analysis and to determine useful performance criteria the author developed a Monte Carlo Simulation which provided a relative comparison of various performance measurement models. Two sets of human performance data were used to demonstrate this new statistical tool.

In this thesis, we will look to predict the performance of a basketball player coming into the NBA depending on where the player was picked in the NBA draft. This will be done by testing different machine learning models on data from the previous 35 NBA drafts and then comparing the models in order to see which model had the highest accuracy of classification. The machine learning methods used are Linear Discriminant Analysis, K-Nearest Neighbors, Support Vector Machines and Random Forests. The results show that the method with the highest accuracy of classification was Random Forests, with an accuracy of 42%.

This research tested the ability of fish morphology to predict membership of fishes in habitat guilds, their swimming performance, and habitat preference. Further, it considered methods for choosing a surrogate species to identify habitat of target species. Morphological discriminant functions were developed using morphological traits of fishes from one river to identify membership in two habitat guild systems (mesohabitat and microhabitat). Functions were then used to test factors influencing classification success of holdout tests and validated using fishes of a second river. Morphology was only partly successful (50%) at predicting membership in habitat guilds. Morphology identified species by shape, i.e., classifying test species into guilds with members of their genus, but not habitat use, because morphology and habitat were not strongly linked through function. By improving guild definition, relationships between morphology and habitat (Froude number) were identified for all fish groups examined (darters, benthic minnows, pelagic minnows, and suckers). Relationships were not transferable among groups. Further, morphology of eight minnows was linked to swimming performance, a key task for using habitat, in lab measurements of critical swimming speeds. In turn, swimming performance was related to habitat (Froude number). Morphology will be most successful at predicting habitat use of fishes when (1) more, discrete guilds are used, (2) guilds are identified within families, (3) variation in lifestyles (benthic vs. pelagic) is considered, and (4) key tasks related to using habitat are strongly associated with morphology. Finally, I examined a phylogenetic approach to identifying useable habitat. Closely related surrogate species were not more accurate in identifying habitat of target species than surrogates chosen by other methods. When a target species used only one mesohabitat, the highest overlap in habitat use occurred with other fishes of the same family using that mesohabitat (within a physiographic province). For target species using several mesohabitat types, surrogates from the next highest taxonomic unit, e.g., genus or subgenus, provided the most accurate information. Ecomorphology offers a mechanistic and defensible method for identifying habitat preferences of fishes and should be more widely considered as a tool for establishing habitat relationships of stream fishes. / Ph. D.

A number of recent successful authorship studies have relied on a statistical analysis of language features based on function words. However, stylometry has not been extensively applied to Elizabethan and Jacobean dramatic questions. To determine the effectiveness of such an approach in this field, language features are studied in twenty-four plays by Shakespeare and eight by Fletcher. The goal is to develop procedures that might be used to determine the authorship of individual scenes in The Two Noble Kinsmen and Henry VIII. Homonyms, spelling variants and contracted forms in old-spelling dramatic texts present problems for a computer analysis. A program that uses a system of pre-edit codes and replacement /expansion lists was developed to prepare versions of the texts in which all forms of common words can be recognized automatically. To evaluate some procedures for determining authorship developed by A. Q. Morton and his colleagues, occurrences of 30 common collocations and 5 proportional pairs are analyzed in the texts. Within-author variation for these features is greater than had been found in previous studies. Univariate chi-square tests are shown to be of limited usefulness because of the statistical distribution of these textual features and correlation between pairs of features. The best of the collocations do not discriminate as well as most of the individual words from which they are composed. Turning to the rate of occurrence of individual words and groups of words, distinctiveness ratios and t-tests are used to select variables that best discriminate between Shakespeare and Fletcher. Variation due to date of composition and genre within the Shakespeare texts is examined. A multivariate and distributionfree discriminant analysis procedure (using kernel estimation) is introduced. The classifiers based on the best marker words and the kernel method are not greatly affected by characterization and perform well for samples as short as 500 words. When the final procedure is used to assign the 459 scenes of known authorship (containing at least 500 words)almost 112 95% are assigned to the correct author. Only two scenes are incorrectly classified, and 4.8% of the scenes cannot be assigned to either author by the procedure. When applied to individual scenes of at least 500 words in The Two Noble Kinsmen and Henry VIII, the procedure indicates that both plays are collaborations and generally supports the usual division. However, the marker words in a number of scenes often attributed to Fletcher are very much closer to Shakespeare's pattern of use. These scenes include TNK IV.iii and H8 I.iii, IV.i-ii and V.iv.

Quality of life (QOL) is an extremely important concept in the promotion of appropriate and successful health care programmes. However, there is a need for conceptual clarity to unravel the complexities of terminology in different medical conditions and the underlying factors that have a direct influence on the quality of life for people with coronary heart disease. The primary objective of this thesis is to propose a theoretical model which specifies the domains of QOL and the interrelationships among these domains. The objectives of the study are four-fold: (1) To examine whether a cardiac rehabilitation programme has a beneficial effect on cardiac heart disease patients; (2) To evaluate the primary components of generic health-related quality of life assessment tools for people with coronary heart disease; (3) To identify the main factors governing disease-specific health-related quality of life assessment tools amongst patients with coronary heart disease; (4) To examine a variety of conceptual models of QOL and to determine their relevance to cardiac patients. First, in order to provide conceptual clarity, a comprehensive review of QOL measures was undertaken. Second, data was collected on a cardiac rehabilitation programme in a county hospital using Short Form-36 (SF-36) and Quality of Life for Myocardial Infarction (QLMI) instruments. This data was analysed using a number of techniques including (l)meta-analysis; (2)discriminant analysis; (3)factor analysis and (4)structural equation modelling. Analysing the data in this way enabled the development and clarification of the specific domains of the quality of life model. Meta-analysis involved pooling the results of several studies, these were then analysed to provide a systematic, quantitative review of the data. The results found that the related studies did not have consistent outcomes to support the positive effects of a cardiac exercise rehabilitation programme on quality of life in coronary patients. Findings from the SF-36 indicate that older people with coronary heart disease gain more pain relief than their younger counterparts. After a cardiac exercise rehabilitation progranune, statistically significant improvements occurred in physical function, social function, role limitation/physical, energy/vitality, body pain, and change in health-related dimensions of quality of life. The first-order five domains model includes the symptom domain, the restriction domain, the confidence domain, the self-esteem domain and the emotion domain. This model represents an appropriate model of quality of life for people with coronary heart disease compared to the three-domain model and the four-domain model. In terms of the second-order QOL model, the five-domain model also has an adequate fit to the data. According to the result of structural equation modelling, three models, including the null model, the alternative model I and the alternative model n, did not fit the data perfectly. However, the construct of full latent variable model gradually increased the fit statistics from the null model to the alternative model I and from the null model to alternative model n. Therefore, it can be concluded that the paths and indicators of the three models need to be further adjusted in order to provide a more appropriate model. Nevertheless, this is a first trial to examine a full model of quality of life for people with coronary heart disease using the structural equation analyses. As such, this study provides a new approach to examining the difference between empirical studies and theoretical approaches.

Approved for public release, distribution is unlimited / This study continues a previous face recognition investigation using uncooled infrared technology. The database developed in an earlier study is further expanded to include 50 volunteers with 30 facial images from each subject. The automatic image reduction method reduces the pixel size of each image from 160 120 to 60 45 . The study reexamines two linear classification methods: the Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA). Both PCA and LDA apply eigenvectors and eigenvalues concepts. In addition, the Singular Value Decomposition based Snapshot method is applied to decrease the computational load. The K-fold Cross Validation is applied to estimate classification performances. Results indicate that the best PCA-based method (using all eigenvectors) produces an average classification performance equal to 79.22%. Incorporated with PCA for dimension reduction, the LDA-based method achieves 94.58% accuracy in average classification performance. Additional testing on unfocused images produces no significant impact on the overall classification performance. Overall results again confirm uncooled IR imaging can be used to identify individual subjects in a constrained indoor environment. / Lieutenant, United States Navy

Before the German reunification, Dresden was a city of motorized traffic and cyclist were rare. But in the 90's began a change of transport policy and cycling became more important. This Master Thesis wants to show the current standing of cycling in Dresden. Thats why the results of the "SrV"-study should be analysed with regard to potential cyclists and their journeys. As methods were used a descriptive analysis and the linear discriminant analysis, both used at a personal and journey-specific level of data. As a result, Dresden have to do much more to become a good "cycling-city", so the bike-level wasn't really high in the year 2013. Instead the car is still the mostly used transport vehicle and the proportion in the Modal-Split is only slowly sinking. But this study shows typical characteritics of cyclists and cycling journays of Dresden, so there is a basis to get more people involved to cycle and become a more eco-friendly city.

We propose a new method to perform functional principal component analysis (FPCA) for discretely observed functional data by solving successive optimization problems. The new framework can be applied to both regularly and irregularly observed data, and to both dense and sparse data. Our method does not require estimates of the individual sample functions or the covariance functions. Hence, it can be used to analyze functional data with multidimensional arguments (e.g. random surfaces). Furthermore, it can be applied to many processes and models with complicated or nonsmooth covariance functions. In our method, smoothness of eigenfunctions is controlled by directly imposing roughness penalties on eigenfunctions, which makes it more efficient and flexible to tune the smoothness. Efficient algorithms for solving the successive optimization problems are proposed. We provide the existence and characterization of the solutions to the successive optimization problems. The consistency of our method is also proved. Through simulations, we demonstrate that our method performs well in the cases with smooth samples curves, with discontinuous sample curves and nonsmooth covariance and with sample functions having two dimensional arguments (random surfaces), repectively. We apply our method to classification problems of retinal pigment epithelial cells in eyes of mice and to longitudinal CD4 counts data. In the second part of this dissertation, we propose a sparse Fisher’s discriminant analysis method with thresholded linear constraints. Various regularized linear discriminant analysis (LDA) methods have been proposed to address the problems of the LDA in high-dimensional settings. Asymptotic optimality has been established for some of these methods when there are only two classes. A difficulty in the asymptotic study for the multiclass classification is that for the two-class classification, the classification boundary is a hyperplane and an explicit formula for the classification error exists, however, in the case of multiclass, the boundary is usually complicated and no explicit formula for the error generally exists. Another difficulty in proving the asymptotic consistency and optimality for sparse Fisher’s discriminant analysis is that the covariance matrix is involved in the constraints of the optimization problems for high order components. It is not easy to estimate a general high-dimensional covariance matrix. Thus, we propose a sparse Fisher’s discriminant analysis method which avoids the estimation of the covariance matrix, provide asymptotic consistency results and the corresponding convergence rates for all components. To prove the asymptotic optimality, we provide an asymptotic upper bound for a general linear classification rule in the case of muticlass which is applied to our method to obtain the asymptotic optimality and the corresponding convergence rate. In the special case of two classes, our method achieves the same as or better convergence rates compared to the existing method. The proposed method is applied to multivariate functional data with wavelet transformations.