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<p> We developed and tested procedures to rank the performance of security analysts according to the timeliness of their earning forecasts. We compared leaders and followers among analysts on various performance attributes, such as accuracy, boldness, experience, brokerage size and so on. We also use discriminant analysis and logistic regression model to examine what attributes have an effect on the classification. Further, we examined whether the timeliness of forecasts is related to their impact on stock prices. We found that the lead analysts identified by the measure of forecast timeliness have a greater impact on stock price than follower analysts. Our initial sample includes all firms on the Institutional Brokers Estimate System (I/B/E/S) database and security return data on the daily CRSP file for the years 1994 through 2003.</p> / Thesis / Master of Science (MSc)

The classification problem in discriminant analysis involves identifying a function that accurately classifies observations as originating from one of two or more mutually exclusive groups. Because no single classification technique works best for all problems, many different techniques have been developed. For business applications, neural networks have become the most commonly used classification technique and though they often outperform traditional statistical classification methods, their performance may be hindered because of failings in the use of training data. This problem can be exacerbated because of small data set size. In this dissertation, we identify and discuss a number of potential problems with typical random partitioning of neural network training data for the classification problem and introduce deterministic methods to partitioning that overcome these obstacles and improve classification accuracy on new validation data. A traditional statistical distance measure enables this deterministic partitioning. Heuristics for both the two-group classification problem and k-group classification problem are presented. We show that these heuristics result in generalizable neural network models that produce more accurate classification results, on average, than several commonly used classification techniques. In addition, we compare several two-group simulated and real-world data sets with respect to the interior and boundary positions of observations within their groups' convex polyhedrons. We show by example that projecting the interior points of simulated data to the boundary of their group polyhedrons generates convex shapes similar to real-world data group convex polyhedrons. Our two-group deterministic partitioning heuristic is then applied to the repositioned simulated data, producing results superior to several commonly used classification techniques. / Ph. D.

Linear discriminant analysis is the classification of an individual as having arisen from one or the other of two populations on the basis of a scalar linear function of measurements of the individual. This paper is a population and large sample study of linear discriminant analysis. The population study is carried out on three levels: (1.1) (a) with loss functions and prior probabilities, (b) without loss functions but with prior probabilities, (c) with neither. The first level leads to consideration of risks which may be split into two components, one for each type of misclassification, i.e. classification of an individual into population I given it arose from population II, and classification of it into II given it arose from I. Similarly, the second level leads to consideration of expected errors and the third level leads to consideration of conditional probabilities of misclassification, both again which may be divided into the same two components. At each level the "optimum" discriminator should jointly minimize the two probability components. These quantities are all positive for all hyperplanes. Either one or any pair may be made equal to zero by classifying all individuals of a sample into the appropriate population; but this maximizes the other one. Consequently, joint minmization must be obtained by some compromise, e.g. by selecting a single criterion to be minimized. Two types of criteria for judging discriminators are considered at each level: (1.4) (i) Total risk (a) (1.5) Total expected errors (b) (1.6) . Sum of conditional probabilities of misclassification (c) (1.7) (ii) Larger risk (a) (1.8) Larger expected error (b) (1.9) Larger conditional probability of misclassification (c). These criteria are not particularly new, but have not been applied to linear discrimination and not been all used jointly. If A is a k-dimensional row vector of direction numbers, X a k-dimensional row vector of variables, and a constant, a linear discriminator is (1.10) AX' = o, which also represents a hyperplane in k-space. An individual is classified as being from one or the other population on the basis of its position relative to the hyperplane. The parameters A and c ot (1.10) were investigated to find those sets of values which minimize each of the two criteria at various levels. Exact results were found for A under some circumstances and approximate results in others. At the levels (b) and (c), when exact results were obtained, they were the same for both criteria and were independent or c. Investigation of the c’s showed the c’s to be exact functions of A and the parameters and yielded one c for each criterion. At level (c), the c's for criteria (i) and (ii), c(min) and c(σ), respectively, were compared to c(m), a population analog of the c suggested by other authors, to discover the conditions under which it was better (i.e. having lesser criteria) than both c(min), c(σ) on criterion (ii), (i) respectively. In the large sample study, variances and covariances were found (in many cases approximately) for all estimates of the parameters entering into the conditional probabilities of misclassification (level (c)). Extension of results to level (b) and to special cases of level (a) were given. From these variances and covariances were derived the expectations of these probabilities for both criteria, at level (c), and comparisons were made where feasible. Results were tabulated. / Ph. D.

In experiments with repeated measurements made on the same subjects, the repeated observations in time may be correlated. Therefore, the assumption of independent observations cannot be made in general. This thesis considers the experimental design with treatments in a two-way classification with a disproportionate number of subjects allocated to each treatment combination and repeated measurements made on the subjects. A procedure is shown to be applicable for computing an analysis under somewhat restrictive assumptions. It is assumed that the variances are equal for all times and the correlations in time are equal. The tests obtained are for the three-factor interaction, the two-factor interactions assuming the three-factor interaction zero, and the main effects assuming all interactions zero. The procedure requires the inverse of one matrix, some matrix multiplication, and the calculation of some standard sums of squares. / Master of Science

No / Sex assessment is key when investigating human remains either from medicolegal contexts or archaeological sites. Sex is usually assessed by examination of the skull and pelvis, but this may not always be possible if skeletal material is fragmented or incomplete. The present study investigated the potential for using carpals to assess sex, utilizing one hundred individuals of known-sex from the Christ Church, Spitalfields Collection, curated at the Natural History Museum (London). A series of newly-defined measurements are applied to all eight carpals. Inter- and intra- observer error tests show that all measurements are satisfactorily reproduced by the first author and another observer. Paired t-tests to investigate side asymmetry of the carpals reveal that some, but not all, measurements are consistently larger on the right hand side than the left. Independent t-tests confirm that all carpals are sexually dimorphic. Univariate measurements produce accuracy levels that range from 64.6 to 84.7%. Stepwise discriminant function analysis, devised separately for left and right sides, provides reliable methods for assessing sex from single and multiple carpals, with an accuracy range of 71.7 to 88.6%. All functions derived are tested for accuracy on a sample of twenty additional individuals from the Christ Church, Spitalfields Collection.

Orientador: Carlos Roberto Padovani / Banca: Adriano Wagner Ballarin / Banca: Flávio Fekkari Aragon / Banca: José Carlos Martinez / Banca: Marie Oshiiwa / Resumo: Aspectos teóricos relacionados à Análise Discriminante Multivariada - Linear e Quadrática - foram discutidos, por meio de um extenso levantamento histórico da função discriminante, com seus primórdios no trabalho de Fisher e sua posterior evolução, enfocando o intenso desenvolvimento das técnicas classificatórias discriminantes com o advento dos computadores. Foi dada ênfase aos softwares estatísticos desenvolvidos para PC, que realizam a análise discriminante, e que representam uma grande contribuição para pesquisadores e usuários desta técnica. Considerando a dificuldade existente quanto a aplicativos computacionais acessíveis a pesquisadores da área de ciências agrárias, elaborou-se um programa que realiza a análise discriminante quadrática com as respectivas freqüências de classificação correta, bem como o manual explicativo do usuário. Verificou-se que a função discriminante quadrática trata de um procedimento bastante útil nas ciências agrárias, como, por exemplo, em estudos nas áreas de solos, cultivos diversos (soja, milho, cana de açúcar, pupunha, braquiária, frutas), criação de animais e classificação e seleção de madeiras; porém, subutilizada frente à dificuldade de programas computacionais de fácil manuseio e acesso a pesquisadores das áreas aplicadas. Os procedimentos estudados e discutidos foram ilustrados com exemplos de aplicação, utilizando dados experimentais agronômicos de espécies de Girassóis e Eucalyptus, submetidos ao aplicativo desenvolvido. / Abstract: A large historical study of the discriminant function has allowed a discussion on theoretical aspects related to the Multivaried Discriminant Analysis - Linear and Quadratic, showing its past in the work of Fisher and its later evolution, emphasizing the wide development of classificatory discriminant techniques with the happening of the computers, and specific statistic softwares which practice the discriminant analysis, representing a big contribution to researches and users of this technique. Considering the difficulty in relation to accessible softwares to researches of the agrarian area, a software which performs a linear and quadratic discriminant analysis was built with its frequencies of correct classification, as well as an explicative manual to users. The quadratic discriminant was studied as being a very useful process in agrarian sciences. Some examples of this usefulness is in studies of the ground, diversified cultivation (soybean, corn, sugarcane, pejibaye, brachiaria decumbens fruits), animal creation and wood selection, and classification; however, misused in relation to the difficulties of easy handing and access to researchers of applied areas. The studied and discussed procedures were illustrated with applications, using agronomic experimental data of Sunflower and Eucalyptus, submitted to developed software. / Doutor

Most of the cardiac structural abnormalities will be examined by echocardiography. With more understanding of heart diseases, it is commonly recognized that heart failures are closely related to left ventricular systolic and diastolic functions. This work discusses the association between gray-scale differences and the risk of heart disease from the changes in left ventricular systole and diastole of ultrasound image. Owing to the large dimension of data matrix, following Chen (2011), we also simplify the influence factors by factor analysis and calculate factor scores to present the characteristics of subjects. Two kinds of classification criteria are used in this work, namely logistic model with distance indicator and discriminant function. According to Guo et al. (2001), we calculate the Mahalanobis distance from each subject to the center of normal and abnormal group, then use logistic model to fit the distances for classification later. This is called logistic model with distance indicator. For the discriminant analysis, the regularized method by Friedman (1989) for estimation of covariance matrix is used, which is more flexible and can improve the covariance matrix estimates when the sample size is small. As far as the cut-point of ROC curve, following the approach as in Hanley et al. (1982), we find the most appropriate cut-point which has good performances for both sensitivity and specificity under the same classification criteria. Then the regularized method and the cut-point of ROC curve are combined to be a new classification criterion. The results under the new classification criterion are presented to classify normal and abnormal groups.

Made available in DSpace on 2014-06-11T19:31:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-12-19Bitstream added on 2014-06-13T21:02:54Z : No. of bitstreams: 1 simeao_sfap_dr_botfca.pdf: 899191 bytes, checksum: da6ed77a45734c278c56395d23c51cd0 (MD5) / Universidade Estadual Paulista (UNESP) / Aspectos teóricos relacionados à Análise Discriminante Multivariada - Linear e Quadrática - foram discutidos, por meio de um extenso levantamento histórico da função discriminante, com seus primórdios no trabalho de Fisher e sua posterior evolução, enfocando o intenso desenvolvimento das técnicas classificatórias discriminantes com o advento dos computadores. Foi dada ênfase aos softwares estatísticos desenvolvidos para PC, que realizam a análise discriminante, e que representam uma grande contribuição para pesquisadores e usuários desta técnica. Considerando a dificuldade existente quanto a aplicativos computacionais acessíveis a pesquisadores da área de ciências agrárias, elaborou-se um programa que realiza a análise discriminante quadrática com as respectivas freqüências de classificação correta, bem como o manual explicativo do usuário. Verificou-se que a função discriminante quadrática trata de um procedimento bastante útil nas ciências agrárias, como, por exemplo, em estudos nas áreas de solos, cultivos diversos (soja, milho, cana de açúcar, pupunha, braquiária, frutas), criação de animais e classificação e seleção de madeiras; porém, subutilizada frente à dificuldade de programas computacionais de fácil manuseio e acesso a pesquisadores das áreas aplicadas. Os procedimentos estudados e discutidos foram ilustrados com exemplos de aplicação, utilizando dados experimentais agronômicos de espécies de Girassóis e Eucalyptus, submetidos ao aplicativo desenvolvido. / A large historical study of the discriminant function has allowed a discussion on theoretical aspects related to the Multivaried Discriminant Analysis - Linear and Quadratic, showing its past in the work of Fisher and its later evolution, emphasizing the wide development of classificatory discriminant techniques with the happening of the computers, and specific statistic softwares which practice the discriminant analysis, representing a big contribution to researches and users of this technique. Considering the difficulty in relation to accessible softwares to researches of the agrarian area, a software which performs a linear and quadratic discriminant analysis was built with its frequencies of correct classification, as well as an explicative manual to users. The quadratic discriminant was studied as being a very useful process in agrarian sciences. Some examples of this usefulness is in studies of the ground, diversified cultivation (soybean, corn, sugarcane, pejibaye, brachiaria decumbens fruits), animal creation and wood selection, and classification; however, misused in relation to the difficulties of easy handing and access to researchers of applied areas. The studied and discussed procedures were illustrated with applications, using agronomic experimental data of Sunflower and Eucalyptus, submitted to developed software.