Dissertations / Theses on the topic 'Principal components analysis'

Mestrado em Finanças
Nesta tese aplicámos a análise de componentes principais ao mercado bolsista português usando os constituintes do índice PSI-20, de Julho de 2008 a Dezembro de 2016. Os sete primeiros componentes principais foram retidos, por se ter verificado que estes representavam as maiores fontes de risco deste mercado em específico. Assim, foram construídos sete portfólios principais e comparámo-los com outras estratégias de alocação. Foram construídos o portfólio 1/N (portfólio com investimento igual para cada um dos 26 ativos), o PPEqual (portfólio com igual investimento em cada um dos 7 principal portfólios) e o portfólio MV (portfólio que tem por base a teoria moderna de gestão de carteiras de Markowitz (1952)). Concluímos que estes dois últimos portfólios apresentavam os melhores resultados em termos de risco e retorno, sendo o portfólio PPEqual mais adequado a um investidor com maior grau de aversão ao risco e o portfólio MV mais adequado a um investidor que estaria disposto a arriscar mais em prol de maior retorno. No que diz respeito ao nível de risco, o PPEqual é o portfólio com melhores resultados e nenhum outro portfólio conseguiu apresentar valores semelhantes. Assim encontrámos um portfólio que é a ponderação de todos os portfólios principais por nós construídos e este era o portfólio mais eficiente em termos de risco.
In this thesis we apply principal component analysis to the Portuguese stock market using the constituents of the PSI-20 index from July 2008 to December 2016. The first seven principal components were retained, as we verified that these represented the major risk sources in this specific market. Seven principal portfolios were constructed and we compared them with other allocation strategies. The 1/N portfolio (with an equal investment in each of the 26 stocks), the PPEqual portfolio (with an equal investment in each of the 7 principal portfolios) and the MV portfolio (based on Markowitz's (1952) mean-variance strategy) were constructed. We concluded that these last two portfolios presented the best results in terms of return and risk, with PPEqual portfolio being more suitable for an investor with a greater degree of risk aversion and the MV portfolio more suitable for an investor willing to risk more in favour of higher returns. Regarding the level of risk, PPEqual is the portfolio with the best results and, so far, no other portfolio has presented similar values. Therefore, we found an equally-weighted portfolio among all the principal portfolios we built, which was the most risk efficient.
info:eu-repo/semantics/publishedVersion

Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Santosh Vempala; Committee Member: Adam Kalai; Committee Member: Haesun Park; Committee Member: Ravi Kannan; Committee Member: Vladimir Koltchinskii. Part of the SMARTech Electronic Thesis and Dissertation Collection.

The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by the industry sector to which the individual stocks belong. Joining together PCA with classical linear regression creates principal components regression which is further in the thesis applied to the German DAX 30 index logarithmic returns forecasting using various macroeconomic and financial predictors. The correlation between two energy stocks returns - Chevron and ExxonMobil is forecasted using orthogonal (or PCA) GARCH. The constructed forecast is then compared with the predictions constructed by the conventional multivariate volatility models - EWMA and DCC GARCH.

Includes summary.
Includes bibliographical references.
Fault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization. Principal component analysis is one of the statistical techniques that are used for fault detection. Determination of the number of PCs to be retained plays a big role in detecting a fault using the PCA technique. In this dissertation focus has been drawn on the methods of determining the number of PCs to be retained for accurate and effective fault detection in a laboratory thermal system. SNR method of determining number of PCs, which is a relatively recent method, has been compared to two commonly used methods for the same, the CPV and the scree test methods.

Thesis (Ph. D.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

Doctor en Ciencias de la Ingeniería, Mención Modelación Matemática
En esta tesis definimos dos adaptaciones no-lineales del análisis de componentes principales, para el estudio de la variabilidad de datos conformados por medidas de probabilidad y por imágenes. En el Capitulo 2 introducimos el método de análisis de componentes principales geodésico (ACPG) en el espacio de medidas de probabilidad en la línea real, con segundo momento finito, dotado de la métrica de Wasserstein. Apoyándonos en la estructura pseudo-riemanniana del espacio de Wasserstein, definimos el ACPG basado en adaptaciones del ACP a variedades, propuestas en la literatura. En este contexto, el ACPG se define por medio de un problema de minimización sobre el espacio conformado por los subconjuntos geodésicos del espacio de Wasserstein. Usando argumentos de compacidad y de gama-convergencia, establecemos la consistencia del método, demostrando que el ACPG converge a su contraparte poblacional, cuando el tamaño de la muestra crece a infinito. Discutimos las ventajas de este método, respecto a un ACP funcional estándar de medidas de probabilidad en el espacio de Hilbert de funciones a cuadrado integrable. Con el fin de mostrar los beneficios de este procedimiento para el análisis de datos, exhibimos algunos ejemplos ilustrativos en un modelo estadístico simple. En el Capitulo 3 describimos el método de análisis de componentes principales geométrico (ACP geométrico) para analizar los modos principales de variación geométrica de un conjunto de imágenes. En este contexto proponemos modelar la variabilidad geométrica de las imágenes, respecto a un patrón medio de referencia, por medio de un operador de deformación parametrizado por un espacio de Hilbert. El ACP geométrico consta de dos etapas: (1) registro de imágenes usando un operador de deformación y (2) ACP estándar en los parámetros asociados a las deformaciones. La consistencia del procedimiento es analizada en el contexto de un modelo estadístico de patrón deformable, con una doble asíntota, donde el número de observaciones tiende a infinito y el ruido aditivo converge a cero. Para destacar los beneficios de este procedimiento, describimos un algoritmo y su aplicación a algunos experimentos numéricos con imágenes reales.

It is often a goal of the risk management of a portfolio of interest rate sensitive instruments to minimize the impact of movements in market rates on the value of the portfolio. This can be done by considering the sensitivity of the portfolio to each of the market rates that are used to bootstrap a yield curve. However, this is likely to lead to an excessive amount of trading due to an investment in a large number of hedging securities. As an alternative, we consider using principal components analysis (PCA) to condense most of the variability in the market rates into a much smaller number of risk factors, called the principal components. One can then construct a hedging portfolio so as to make the portfolio immune to shocks in these principal components, and hence to the most common movements in the yield curve. We compare the effectiveness of these two hedging strategies for hedging a portfolio of interest-rate options, both in the absence and presence of transaction costs. We also consider the additional feature of being able to update each hedging methodology on a daily basis and rebalance the hedge portfolios accordingly.

In principal component analysis (PCA), the principal components (PC) are linear combinations of the variables that minimize some objective function. In the classical setup the objective function is the variance of the PC's. The variance of the PC's can be easily upset by outlying observations; hence, Chen and Li (1985) proposed a robust alternative for the PC's obtained by replacing the variance with an M-estimate of scale. This approach cannot achieve a high breakdown point (BP) and efficiency at the same time. To obtain both high BP and efficiency, we propose to use MM- and τ-estimates in place of the M-estimate. Although outliers may cause bias in both the direction and the size of the PC's, Chen and Li looked at the scale bias only, whereas we consider both. All proposed robust methods are based on the minimization of a non-convex objective function; hence, a good initial starting point is required. With this in mind, we propose an orthogonal version of the least median of squares (Rousseeuw and Leroy, 1987) and a new method that is orthogonal equivariant, robust and easy to compute. Extensive Monte Carlo study shows promising results for the proposed method. Orthogonal regression and detection of multivariate outliers are discussed as possible applications of PCA.
Science, Faculty of
Statistics, Department of
Graduate

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2005.
Victoria Chen, Committee Co-Chair ; Kwok Tsui, Committee Chair ; Janet Allen, Committee Member ; David Goldsman, Committee Member ; Roshan Vengazhiyil, Committee Member. Vita. Includes bibliographical references.

Thesis (PhD)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: When estimating the covariance matrices of two or more populations, the covariance matrices are often assumed to be either equal or completely unrelated. The common principal components (CPC) model provides an alternative which is situated between these two extreme assumptions: The assumption is made that the population covariance matrices share the same set of eigenvectors, but have di erent sets of eigenvalues. An important question in the application of the CPC model is to determine whether it is appropriate for the data under consideration. Flury (1988) proposed two methods, based on likelihood estimation, to address this question. However, the assumption of multivariate normality is untenable for many real data sets, making the application of these parametric methods questionable. A number of non-parametric methods, based on bootstrap replications of eigenvectors, is proposed to select an appropriate common eigenvector model for two population covariance matrices. Using simulation experiments, it is shown that the proposed selection methods outperform the existing parametric selection methods. If appropriate, the CPC model can provide covariance matrix estimators that are less biased than when assuming equality of the covariance matrices, and of which the elements have smaller standard errors than the elements of the ordinary unbiased covariance matrix estimators. A regularised covariance matrix estimator under the CPC model is proposed, and Monte Carlo simulation results show that it provides more accurate estimates of the population covariance matrices than the competing covariance matrix estimators. Covariance matrix estimation forms an integral part of many multivariate statistical methods. Applications of the CPC model in discriminant analysis, biplots and regression analysis are investigated. It is shown that, in cases where the CPC model is appropriate, CPC discriminant analysis provides signi cantly smaller misclassi cation error rates than both ordinary quadratic discriminant analysis and linear discriminant analysis. A framework for the comparison of di erent types of biplots for data with distinct groups is developed, and CPC biplots constructed from common eigenvectors are compared to other types of principal component biplots using this framework. A subset of data from the Vermont Oxford Network (VON), of infants admitted to participating neonatal intensive care units in South Africa and Namibia during 2009, is analysed using the CPC model. It is shown that the proposed non-parametric methodology o ers an improvement over the known parametric methods in the analysis of this data set which originated from a non-normally distributed multivariate population. CPC regression is compared to principal component regression and partial least squares regression in the tting of models to predict neonatal mortality and length of stay for infants in the VON data set. The tted regression models, using readily available day-of-admission data, can be used by medical sta and hospital administrators to counsel parents and improve the allocation of medical care resources. Predicted values from these models can also be used in benchmarking exercises to assess the performance of neonatal intensive care units in the Southern African context, as part of larger quality improvement programmes.
AFRIKAANSE OPSOMMING: Wanneer die kovariansiematrikse van twee of meer populasies beraam word, word dikwels aanvaar dat die kovariansiematrikse of gelyk, of heeltemal onverwant is. Die gemeenskaplike hoofkomponente (GHK) model verskaf 'n alternatief wat tussen hierdie twee ekstreme aannames gele e is: Die aanname word gemaak dat die populasie kovariansiematrikse dieselfde versameling eievektore deel, maar verskillende versamelings eiewaardes het. 'n Belangrike vraag in die toepassing van die GHK model is om te bepaal of dit geskik is vir die data wat beskou word. Flury (1988) het twee metodes, gebaseer op aanneemlikheidsberaming, voorgestel om hierdie vraag aan te spreek. Die aanname van meerveranderlike normaliteit is egter ongeldig vir baie werklike datastelle, wat die toepassing van hierdie metodes bevraagteken. 'n Aantal nie-parametriese metodes, gebaseer op skoenlus-herhalings van eievektore, word voorgestel om 'n geskikte gemeenskaplike eievektor model te kies vir twee populasie kovariansiematrikse. Met die gebruik van simulasie eksperimente word aangetoon dat die voorgestelde seleksiemetodes beter vaar as die bestaande parametriese seleksiemetodes. Indien toepaslik, kan die GHK model kovariansiematriks beramers verskaf wat minder sydig is as wanneer aanvaar word dat die kovariansiematrikse gelyk is, en waarvan die elemente kleiner standaardfoute het as die elemente van die gewone onsydige kovariansiematriks beramers. 'n Geregulariseerde kovariansiematriks beramer onder die GHK model word voorgestel, en Monte Carlo simulasie resultate toon dat dit meer akkurate beramings van die populasie kovariansiematrikse verskaf as ander mededingende kovariansiematriks beramers. Kovariansiematriks beraming vorm 'n integrale deel van baie meerveranderlike statistiese metodes. Toepassings van die GHK model in diskriminantanalise, bi-stippings en regressie-analise word ondersoek. Daar word aangetoon dat, in gevalle waar die GHK model toepaslik is, GHK diskriminantanalise betekenisvol kleiner misklassi kasie foutkoerse lewer as beide gewone kwadratiese diskriminantanalise en line^ere diskriminantanalise. 'n Raamwerk vir die vergelyking van verskillende tipes bi-stippings vir data met verskeie groepe word ontwikkel, en word gebruik om GHK bi-stippings gekonstrueer vanaf gemeenskaplike eievektore met ander tipe hoofkomponent bi-stippings te vergelyk. 'n Deelversameling van data vanaf die Vermont Oxford Network (VON), van babas opgeneem in deelnemende neonatale intensiewe sorg eenhede in Suid-Afrika en Namibi e gedurende 2009, word met behulp van die GHK model ontleed. Daar word getoon dat die voorgestelde nie-parametriese metodiek 'n verbetering op die bekende parametriese metodes bied in die ontleding van hierdie datastel wat afkomstig is uit 'n nie-normaal verdeelde meerveranderlike populasie. GHK regressie word vergelyk met hoofkomponent regressie en parsi ele kleinste kwadrate regressie in die passing van modelle om neonatale mortaliteit en lengte van verblyf te voorspel vir babas in die VON datastel. Die gepasde regressiemodelle, wat maklik bekombare dag-van-toelating data gebruik, kan deur mediese personeel en hospitaaladministrateurs gebruik word om ouers te adviseer en die toewysing van mediese sorg hulpbronne te verbeter. Voorspelde waardes vanaf hierdie modelle kan ook gebruik word in normwaarde oefeninge om die prestasie van neonatale intensiewe sorg eenhede in die Suider-Afrikaanse konteks, as deel van groter gehalteverbeteringprogramme, te evalueer.

Determining the optimal long-term operating policy of a multi-reservoir power system requires solution of a stochastic nonlinear programming problem. For small systems, the solution can be found by dynamic programming, but for large systems no direct solution method exists yet, so that one must resort to mathematical manipulations to solve the problem. This thesis presents a very efficient procedure for the case where high correlation exists between the state variables. It consists in performing principal components analysis on the trajectories to find a reduced model of the system. The reduced model is then substituted into the operating problem and the resulting problem is solved by stochastic dynamic programming. The reservoir trajectories on which principal components analysis are performed can be obtained by solving the operating problem deterministically for a large number of equally likely flow sequences. The results of applying the manipulation to Quebec's La Grande river, which has four reservoirs, are reported. A comparison with the classical dynamic programming, that is without any reduction, is also studied and results are reported to show the efficiency of the principal components approach.

Orientador: Mary Ann Foglio
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Odontologia de Piracicaba
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Resumo: A Pterodon pubescens Benth, conhecida como sucupira é nativa das regiões que consiste o cerrado brasileiro, nos estados de Minas Gerais, São Paulo, Goiás e Mato Grosso do Sul. Nosso grupo iniciou estudos com a espécie Pterodon pubescens Benth em 1998. Inicialmente foram desenvolvidos estudos relacionado a eficácia dos seus extratos e determinação dos princípios ativos. O objetivo deste trabalho foi avaliar o efeito do calor sobre o processo de formação e degradação dos componentes vouacapanos nos frutos de Pterodon pubescens Benth. e sua influência sobre a atividade antiproliferativa in vitro em células tumorais humanas. Os frutos de P. pubescens foram coletados no município de São Carlos, localizado no estado de São Paulo, e no município de Ponto Chique, localizado no estado de Minas Gerais no período de agosto e setembro de 2012. Mensalmente foram preparados extratos por sistema Soxhlet e maceração dinâmica com as amostras de frutos provenientes dos municípios de São Carlos e de Ponto Chique armazenados em estufa, analisados por cromatografia gasosa capilar acoplada a um detector seletivo de massas e tiveram suas atividades antiproliferativas contra linhagem tumoral humana (PC-3) avaliadas por método de sulforrodamina B. Todos os resultados de teores dos compostos de interesse obtidos por CG-EM foram submetidos a análise de variância de uma única via (ANOVA), considerando-se como nível crítico p? 0,05 para que seja considerados diferença significante entre os grupos estudados, seguidos do Teste de Duncan. Para a análise dos compostos que possuem uma maior influência na atividade antiproliferativa do nos extratos obtidos foi utilizado a Análise de Componentes Principais. Foi possível concluir que há uma possível variação genética das espécies de Pterodon pubescens Benth., e que o calor não influenciou na formação do 6?- hidróxi- 7?- acetoxivouacapano, porém influencia no teor dos outros compostos estudados
Abstract: Pterodon pubescens Benth (Fabeacea) chemical variability was investigated to understand if the secondary metabolites 6?-acetoxi 7?-hydroxy-vouacapan content variability, was produced as an artefact overtime or was characteristic of genotype variability. The oil was extracted both by soxhlet system and at room temperature with stirring. The chemical composition was monitored by gas chromatography-mass spectrometry. The samples were tested in an anticancer assay against prostate (PC-03) human cancer cell line. The response parameter (TGI) was calculated. The chemical variability data was evaluate using the statistical analysis using Principal Component Analysis (PCA) to understand which of the extract's components had a real impact on the in vitro antiproliferative activity. Hierarchical Cluster Analysis (HCA) was performed to visualize the similarities among the extracts components with TGI values. Unscrumbler® v. 9.7 software was employed for analysis. The chemical composition of samples from São Carlos, São Paulo and Ponto Chique, Minais Gerais were monitored monthly during one year for Geranilgeraniol, 6?-acetoxi 7?-hydroxy-vouacapan, 6?- hydroxy-7?-acetoxy-vouacapan-17?-oate methyl ester and 6?-acetoxy-7?-hydroxyvouacapan- 17?-oate methyl ester. In vitro cytotoxicity screening against human prostate cancer cell line displayed higher selectivity and potent anticancer activity with TGI 11.43 mg ml?1 when higher 6?-acetoxi 7?-hydroxy-Vouacapan over total Voaucapan ratio (3.14) was achieved. Nevertheless 6?-acetoxi 7?-hydroxyvouacapan maintained approximately the same content throughout the year among the samples in opposition to overall Voaucapan content. The total Vouacapan total content was directly proportional to Geranygeraniol content decrease content. Samples from São Carlos at time zero had 26% geranylgeraniol content whereas Minas Gerais samples contained the highest content of 1.3%. Throughout the stability test geranylgeraniol concentration decreased with a straight relationship of overall increase of vouacapan content. This study highlights the complexity of factors involved in the production of secondary metabolites in plants. To enable the development of an herbal medicine, one of the greatest challenges is plant input standardization in order to meet efficacy, safety and reproducibility final product's requirements as recommended by Brazilian Sanitary Regulatory Agency (ANVISA). Data presented herein suggest that 6?-acetoxi 7?-hydroxy-vouacapan content variability has a straight relationship with genotype other than produced as artefact overtime
Mestrado
Farmacologia, Anestesiologia e Terapeutica
Mestre em Odontologia

In recent years, massive data sets are generated in many areas of science and business, and are gathered by using advanced data acquisition techniques. New approaches are therefore required to facilitate effective data management and data analysis in this big data era, especially to analyze multidimensional data for real-time applications. This thesis aims at developing generic and effective algorithms for compressing and recovering online multidimensional data, and applying such algorithms in image processing and other related areas. Since multidimensional data are usually represented by tensors, this research uses multilinear algebra as the mathematical foundation to facilitate development. After reviewing the techniques of singular value decomposition (SVD), principal component analysis (PCA) and tensor decomposition, this thesis deduces an effective multilinear principal component analysis (MPCA) method to process such data by seeking optimal orthogonal basis functions that map the original tensor space to a tensor subspace with minimal reconstruction error. Two real examples, 3D data compression for positron emission tomography (PET) and offline fabric defect detection, are used to illustrate the tensor decomposition method and the deduced MPCA method, respectively. Based on the deduced MPCA method, this research develops an incremental MPCA (IMPCA) algorithm which targets at compressing and recovering online tensor objects. To reduce computational complexity of the IMPCA algorithm, this research investigates the low-rank updates of singular values in the matrix and tensor domains, which leads to the development of a sequential low-rank update scheme similar to the sequential Karhunen-Loeve algorithm (SKL) for incremental matrix singular value decomposition, a sequential low-rank update scheme for incremental tensor decomposition, and a quick subspace tracking (QST) algorithm to further enhance the low-rank updates of singular values if the matrix is positive-symmetric definite. Although QST is slightly inferior to the SKL algorithm in terms of accuracy in estimating eigenvector and eigenvalue, the algorithm has lower computational complexity. Two fast incremental MPCA (IMPCA) algorithms are then developed by incorporating the SKL algorithm and the QST algorithm separately into the IMPCA algorithm. Results obtained from applying the developed IMPCA algorithms to detect anomalies from online multidimensional data in a number of numerical experiments, and to track and reconstruct the global surface temperature anomalies over the past several decades clearly confirm the excellent performance of the algorithms. This research also applies the developed IMPCA algorithms to solve an online fabric defect inspection problem. Unlike existing pixel-wise detection schemes, the developed algorithms employ a scanning window to extract tensor objects from fabric images, and to detect the occurrence of anomalies. The proposed method is unsupervised because no pre-training is needed. Two image processing techniques, selective local Gabor binary patterns (SLGBP) and multi-channel feature combination, are developed to accomplish the feature extraction of textile patterns and represent the features as tensor objects. Results of experiments conducted by using a real textile dataset confirm that the developed algorithms are comparable to existing supervised methods in terms of accuracy and computational complexity. A cost-effective parallel implementation scheme is developed to solve the problem in real-time.
published_or_final_version
Industrial and Manufacturing Systems Engineering
Doctoral
Doctor of Philosophy

This thesis introduces the application of discrete Principal Component Analysis (PCA) to corporate governance research. Given the presence of many discrete variables in typical governance studies, I argue that this method is superior to standard PCA that has been employed by others working in the area. Using a dataset of 244 companies listed on the London Stock Exchange in the year 2002-2003, I find that Pearson's correlations underestimate the strength of association between two variables, when at least one of them is discrete. Accordingly, standard PCA performed on the Pearson correlation matrix results in biased estimates. Applying discrete PCA on the polychoric correlation matrix, I extract from 28 corporate governance variables 10 significant factors. These factors represent 8 main aspects of the governance system, namely auditor reputation, large shareholder influence, size of board committees, social responsibility, risk optimisation, director independence level, female representation and institutional ownership. Finally, I investigate the relationship between corporate governance and a firm's long-run share market performance, with the former being the factors extracted. Consistent with Demsetz' (1983) argument, I document limited explanatory power for these governance factors.

Motivated by dimension reduction in regression analysis and signal detection, we investigate order determination for large dimensional matrices with spiked structures in which the dimensions of the matrices are proportional to the sample sizes. Because the asymptotic behaviors of the estimated eigenvalues differ completely from those in fixed dimension scenarios, we then discuss the largest possible order, say q, we can identify and introduce criteria for different settings of q. When q is assumed to be fixed, we propose a "valley-cliff" criterion with two versions - one based on the original differences of eigenvalues and the other based on the transformed differences - to reduce the effect of ridge selection in the criterion. This generic method is very easy to implement and computationally inexpensive, and it can be applied to various matrices. As examples, we focus on spiked population models, spiked Fisher matrices and factor models with auto-covariance matrices. For the case of divergent q, we propose a scale-adjusted truncated double ridge ratio (STDRR) criterion, where a scale adjustment is implemented to deal with the bias in scale parameter for large q. Again, examples include spiked population models, spiked Fisher matrices. Numerical studies are conducted to examine the finite sample performances of the method and to compare it with existing methods. As for theoretical contributions, we investigate the limiting properties, including convergence in probability and central limit theorems, for spiked eigenvalues of spiked Fisher matrices with divergent q. Keywords: Auto-covariance matrix, factor model, finite-rank perturbation, Fisher matrix, principal component analysis (PCA), phase transition, random matrix theory (RMT), ridge ratio, spiked population model.

Thesis (M.S.)--West Virginia University, 2002.
Title from document title page. Document formatted into pages; contains vi, 67 p. : ill. (some col.), col. maps. Includes abstract. Includes bibliographical references (p. 65-67).

Orientador: Roberto Carvalheiro
Coorientador: Danísio Prado Munari
Coorientador: Haroldo Henrique de Rezende Neves
Resumo: A análise de componentes principais (ACP) é uma técnica da estatística multivariada usada para avaliar as relações entre diferentes características a fim de eliminar a redundância resultante de suas correlações. No melhoramento genético animal, a ACP tem sido usada para explorar possíveis interpretações biológicas associadas aos componentes principais (CPs) que podem levar a caracterização de diferentes biotipos de animais. Os objetivos do presente estudo foram: i) avaliar as relações entre características de crescimento, escore visual e reprodutiva, por meio de ACP; ii) identificar, por meio de estudo de associação genômica ampla (GWAS), regiões genômicas que diferenciam os animais quanto aos diferentes componentes; e iii) avaliar a habilidade de predição de valores genéticos genômicos (GEBVs) obtidos para os CPs. Foram utilizados dados fenotípicos de 355.524 animais da raça Nelore provenientes da base de dados Aliança Nelore. Destes, foram genotipados 3.382 animais em painel lllumina® BovineHD (HD, ~777.000 SNPs) e 137 animais em painel GeneSeek Genomic Profiler Bovine HD (~76.000 SNPs). Os animais genotipados com o painel GGP-HD tiveram seus genótipos imputados para o painel mais denso (HD). Após o controle de qualidade, 3.519 animais com informações genotípicas de 471.880 SNPs permaneceram nas análises. A ACP foi realizada utilizando-se a matriz de (co)variância genética aditiva (AT) obtida a partir de análise multi-característica. As estimativas dos efeitos dos SNPs fora... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Principal component analysis (PCA) is a multivariate statistical technique that allows evaluating relationships among different traits in order to eliminate the redundancy resulting from their correlations. In animal breeding, PCA has been used to explore possible biological interpretations associated with the principal components (PCs) that can lead to the characterization of distinguished animal's biotype. The objectives of the present study were: i) to evaluate relationships among growth, visual scores, and reproductive traits by performing a PCA; ii) to identify genomic regions associated with PCs by performing a genome-wide association study (GWAS) on the main PCs; and iii) to evaluate the prediction ability of genomic breeding values (GEBVs) obtained for the PCs. Phenotypic data from 355,524 Nellore animals provided by the Alliance Nellore database, were used in this investigation. A total of 3,382 Nellore animals were genotyped using the lllumina® BovineHD chip (HD, ~777,000 SNPs) and 137 animals were genotyped using the GeneSeek Genomic Profiler Bovine HD chip (~76,000 SNPs). The GGP-HD genotypes were imputed to the HD genotypes. After genomic data quality control, 471,880 SNPs from 3,519 animals were available. The PCA was applied on the additive genetic (co)variance matrix (AT) obtained using multi-trait analysis. For GWAS, SNP effects were estimated using the weighted single-step GBLUP and the BayesC methods. The genes identified within the top-10 ranking windows that explained the highest proportion of variance were used for further functional analyses. For the genomic prediction study, the GEBVs were predicted using three distinguish response variables: EBV of the original traits, EBV of the PCs, and EBV of a selection index used by some Nellore cattle commercial breeding programs. The geno... (Complete abstract click electronic access below)
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O objetivo do presente trabalho consistiu em caracterizar a diversidade fenotípica em genótipos de pinhão-manso através de análises multivariadas, além de desenvolver um modelo de calibração para a determinação do teor de óleo por meio da espectroscopia do infravermelho próximo (NIR) visando a seleção de genótipos com alto teor de óleo. Para a análise fenotípica, foram avaliados 11 caracteres agronômicos em 165 genótipos de pinhão-manso pertencentes ao banco de germoplasma in vivo localizado no Departamento de Produção Vegetal da UNESP/FCAV, Jaboticabal, SP. Os caracteres utilizados foram: altura da planta, diâmetro do caule, número de ramificações primárias, comprimento de fruto, largura de fruto, peso de fruto, formato de fruto, comprimento de semente, largura de semente, peso de semente e teor de óleo. Os dados foram submetidos à análise de componentes principais (PCA), análise de agrupamento pelo método hierárquico de Ward e método não hierárquico k-médias. O caráter formato de fruto foi retirado das análises multivariadas por ser de natureza qualitativa. Na PCA, os 4 CPs explicaram 71,62% da variância total. Os caracteres de maior contribuição para a discriminação dos genótipos no CP1 foram peso de semente, largura de fruto, comprimento de fruto e peso de fruto. Destacaram-se 22 genótipos promissores com potencial para serem explorados em programas de melhoramento genético. O dendrograma obtido pela análise de agrupamento de Ward e para a análise de k-médias geraram nove grupos, dos quais 5 agruparam genótipos com características vantajosas. As análises multivariadas permitiram a caracterização dos genótipos, possibilitando a seleção para muitos caracteres avaliados e a indicação de estratégias de ganhos com a seleção, para programas de melhoramento genético. Para a construção da curva de calibração e validação para teor de óleo, foram utilizadas sementes de 174 plantas, que tiveram o óleo extraído pelo método "Soxhlet" para obtenção dos valores de referência, que foram entre 50,27% a 63,21% (m/m) de óleo. Os espectros foram coletados das sementes sem tegumento, intactas, por acessório de rotação, por meio da espectroscopia por infravermelho próximo (NIR) em equipamento modelo Tango, Bruker. Cada espectro foi retirado com uma média de 64 scans com resolução de 16 cm-1. As regiões espectrais selecionadas para a curva de calibração foram de 8992 a 7424 cm-1 e 5336 a 4928 cm-1. Os coeficientes de correlação (R2) da curva construída foram de 73,23% para calibração e de 70,79% para validação, sendo o erro quadrado médio de calibração (RMSEC) de 1,35 e o erro quadrado médio de validação cruzada (RMSECV) de 1,40 entre os dados obtidos pelo método de referência e pelo NIR. Os resultados indicaram que o teor de óleo da espécie Jatropha curcas L. pode ser estimado por NIR, e o método de referência utilizado foi satisfatório, apresentando boa correlação com a informação espectral, tornando a curva construída confiável. Contudo, futuramente é necessária a adição de novos valores para a melhoria da sua robustez e amplitude.
The purpose of this study was to characterize the phenotypic diversity in Jatropha genotypes by multivariate analysis, and developing a calibration model for determination oil content by Near-infrared spectroscopy (NIR) in order to select genotypes with high oil content. For the phenotypic analysis, 11 agronomic characters were evaluated in 165 genotypes of Jatropha belonging to the germplasm bank in vivo located in the Department of Plant Production UNESP / FCAV, Jaboticabal, SP. The characters were: plant height, stem diameter, number of primary branches, fruit length, fruit width, fruit weight, fruit shape, seed length, seed width, seed weight and oil content. The data were submitted to principal component analysis (PCA), cluster analysis by the hierarchical method of Ward and non- hierarchical method of k-means. The character fruit shape was removed from multivariate analysis for being qualitative. In PCA, the 4 PCs explained 71.62% of the total variance. The greatest contribution of the characters to discriminate the genotypes in PC1 were seed weight, fruit width, fruit length and fruit weight. They highlighted 22 promising genotypes with potential to be exploited in breeding programs. The dendrogram obtained by Ward cluster analysis and the k-means analysis generated nine groups, 5 of which grouped genotypes with advantageous features. Multivariate analyzes allowed the characterization of the genotypes, allowing the selection for many traits and indicating gains strategies with selection for genetic improvement programs. For the construction of the calibration and validation curve for oil content of seeds were used 174 plants that had oil extracted by the method "Soxhlet" to obtain reference values which were between 50.27 to 63% 21% (m/m) of oil. The spectra were collected from intact seeds without husk by attachment rotation through near-infrared spectroscopy (NIR) in Tango machine model, Bruker. Each spectrum was taken with an average of 64 scans with a resolution of 16 cm-1. The spectral regions chosen for the calibration curve were 8992-7424 cm-1 and 5336-4928 cm-1. The correlation coefficients (R2) of the curve constructed were 73,23% for calibration and 70,79% for validation, the calibration mean square error (RMSEC) was 1,35 and cross validation mean square error (RMSECV) was 1,40 between the data obtained by the reference method and the NIR. The results indicated that the oil content of Jatropha curcas L. can be estimated by NIR, and the reference method was satisfactory, with good correlation with the spectral information, making the built curve reliable. However, future addition of new value to improve their robustness and amplitude is required.

Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xvi, 264 p.; also includes graphics. Includes bibliographical references (p. 257-264). Available online via OhioLINK's ETD Center

Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: portfolio risk decomposition; principal components regression; principal components analysis; mortgage-backed securities. Includes bibliographical references (p. 88-89).

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of risk. The results shows that the logistic regression outperforms the discriminant analysis in classifying all of the groups except for the middle one. We conclude that this is in line with previous studies.

Canonical variate analysis (CVA) is a widely used method for analyzing group structure in multivariate data. It is mathematically equivalent to a one-way multivariate analysis of variance and often goes by the name of canonical discriminant analysis. Change over time is a central feature of many phenomena of interest to researchers. This dissertation extends CVA to longitudinal data. It develops models whose purpose is to determine what is changing and what is not changing in the group structure. Three approaches are taken: a maximum likelihood approach, a least squares approach, and a covariance structure analysis approach. All methods have in common that they hypothesize canonical variates which are stable over time. The maximum likelihood approach models the positions of the group means in the subspace of the canonical variates. It also requires modeling the structure of the within-groups covariance matrix, which is assumed to be constant or proportional over time. In addition to hypothesizing stable variates over time, one can also hypothesize canonical variates that change over time. Hypothesis tests and confidence intervals are developed. The least squares methods are exploratory. They are based on three-mode PCA methods such as the Tucker2 and parallel factor analysis. Graphical methods are developed to display the relationships between the variables over time. Stable variates over time imply a particular structure for the between-groups covariance matrix. This structure is modeled using covariance structure analysis, which is available in the SAS package Proc Calis. Methods related to CVA are also discussed. First, the least squares methods are extended to canonical correlation analysis, redundancy analysis, Procrustes rotation and correspondence analysis with longitudinal data. These least squares methods lend themselves equally well to data from multiple datasets. Lastly, a least squares method for the common principal components model is developed.
Ph. D.

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A vegetação do bioma Cerrado é predominantemente constituída por fisionomias savânicas (cerrado lato sensu). Os fatores que determinam a distribuição das diferentes fisionomias estão em constante debate, e têm sido relacionados principalmente a gradientes de fertilidade, profundidade do solo, disponibilidade hídrica, ocorrência de fogo, pressão antrópica e topografia. Entretanto, não são raros os estudos que têm encontrado resultados antagônicos ou não alinhados a esses. Neste sentido, com este estudo, pretendeu-se contribuir para o entendimento dessas questões, buscando responder às seguintes perguntas: existem diferenças florísticas e estruturais na vegetação e nos atributos do solo entre fisionomias previamente classificadas como cerradão e cerrado ralo? O cerradão ocorre em solos mais férteis e/ou com condições físicas menos restritivas que aquelas onde ocorre o cerrado ralo? Para isso, relacionamos atributos físicos e químicos do solo, assim como os padrões florísticos (riqueza e composição de espécies) e estruturais (densidade, área basal, etc.) da comunidade vegetal de três fragmentos de cerrado (cerradão, cerradão “degradado” e cerrado ralo) do nordeste do Maranhão, localizados muito próximos um do outro, em solos taxonomicamente iguais e, portanto, sujeitos a semelhantes influências ambientais e fatores pedogenéticos. Os fragmentos estudados estão inseridos num mosaico de paisagem constituído por remanescentes de áreas de cerrados em níveis diferenciados de degradação. Para as análises do solo, em cada fragmento de cerrado, foram retiradas 49 amostras do solo na camada de 0-0,10 m de profundidade, distribuídas em uma malha amostral quadrada com espaçamento regular de 15 m. As amostras foram coletadas no ponto de cruzamento entre as linhas da malha amostral. As coletas foram realizadas entre os meses de abril e maio de 2013. O levantamento fitossociológico foi realizado ...
The vegetation of the Cerrado biome (Brazilian savanna) is predominantly composed of cerrado physiognomies (cerrado lato sensu). The determining factors on the cerrado physiognomies distribution have been continuously in question, and have been primarily related to gradients of fertility, soil depth, water availability, occurrence of fire, human disturbance and topography. However, there are few studies that have found opposing results or not aligned to these. In this sense, this study was intended to contribute to understanding of these issues, seeking to answer the following questions: Are there floristic and structural differences in vegetation and soil properties faces previously classified as cerradão and cerrado-ralo? Does cerradão occur on soils that are more fertile and/or in mild physical conditions than the cerrado-ralo? For this, we compared physical and chemical soil attributes, as well as floristic patterns (richness and species composition) and structural (density, basal area) of the plant community of three fragments of savanna (cerrado, cerrado degradado and cerrado-ralo) of Northeastern state of Maranhão, located very close to each other in taxonomically identical soils, and therefore subject to similar environmental influences and pedogenic factors. The studied fragments are embedded in a landscape mosaic consisting of remaining areas of cerrado in different levels of degradation. For soil analysis, 49 samples were taken in each fragment in the 0-0.10m soil profile, distributed in a square sampling grid with regular spacing of 15 m. Samples were collected at the point of intersection between the lines of the sampling grid. Sampling was carried out between the months of April and May 2013. Phytosociological survey was carried out in 49 circular plots, in which center the soil sample points were located. A sample of plant material was carried out between the months of January 2012 and May 2013. The ...

A soja está entre as principais culturas mundiais, uma vez que é uma excelente fonte de proteínas e óleo. Além disso, a espécie é aproveitada também pela indústria de biocombustíveis. Considerando a importância das novas mudanças climáticas no agronegócio; para a soja, esta situação é agravada em virtude das condições de temperatura e latitude recomendadas para a semeadura. Dessa forma, para aumentar a produtividade da cultura mesmo frente ao aquecimento global, fazse fundamental o desenvolvimento de cultivares com alta produtividade e tolerantes às altas temperaturas. Neste contexto, o objetivo geral deste trabalho foi selecionar genótipos de soja tolerantes ao calor. Uma população composta por 80 PI\'s de soja e 15 testemunhas foi avaliada sob condições de altas temperaturas, com experimentos instalados nas cidades de Teresina-PI, Piracicaba-SP e Jaboticabal- SP, no ano agrícola 2013/2014. Para a avaliação dos genótipos, foram realizadas análises univariadas e multivariadas. A seleção dos genótipos mais tolerantes a altas temperaturas foi realizada via análise de componentes principais. Nas análises de variâncias univariadas, todos os caracteres mostraram efeitos de tratamentos significativos pelo teste F. Pela análise de componentes principais no experimento conduzido em Teresina-PI, os caracteres que mais contribuíram para a variabilidade dos genótipos avaliados foram: data que metade da parcela atingiu o estádio R5, altura da planta na maturidade, período de granação e valor agronômico. Em Piracicaba-SP, os caracteres que mais contribuíram para a variabilidade foram o período de granação, massa de 100 sementes e o número de dias para a maturidade. Para a seleção dos genótipos mais tolerantes ao calor em Jaboticabal- SP, considerou-se principalmente a altura e a produtividade. Para a análise de mapeamento associativo, a fenotipagem foi realizada em Teresina-PI e avaliados quatro caracteres: altura da planta na maturidade, valor agronômico, massa de cem sementes e produtividade. A genotipagem foi realizada utilizando o chip da empresa Affymetrix. O desequilíbrio de ligação entre pares de marcadores foi calculado pelo coeficiente de determinação r2 e a análise de associação entre marcadores e o fenótipo de interesse foi realizada utilizando a abordagem de modelo linear generalizado. Foram identificadas 16 associações significativas.
Soybean (Glycine max) is one of most important crops in the world. This crop is an source of protein and oil. Beyond that, the species is also utilized for the biofuels industry. The recent climate changes are important on agribusiness, the ones on soybean crop are worse than on other crops because of the conditions of temperature and latitude recommended for planting. Thus, to increase the productivity of the crop even in face of global warming, it is essential that soybean breeding programs promote the development of cultivars highly productive and tolerant to high temperatures. In this context, the aim of this study was to select genotypes for heat tolerance. A population composed of 80 soybean PI\'s and 15 experimental checks was evaluated under high temperature conditions. The experiments were conducted in the cities of Teresina-PI, Piracicaba-SP and Jaboticabal-SP, in the 2013/2014 season. For the evaluation of the genotypes, univariate and multivariate analysis were performed, and the selection of the most genotypes for heat tolerance was performed by principal component analysis (PCA). In the univariate analyzes of variance, all characters showed significant effects of treatments by test F. In the PCA in the experiment conducted in Teresina-PI, the variables that most contributed to the variability of genotypes were: date in which half of the parcel reached R5 stage, height of the plant at maturity, grain filling period and agronomic value. In Piracicaba-SP PCA, the variables that most contributed to the variability were: grain filling period, 100-grain weight and the number of days to maturity. For the selection of the most heat-tolerant genotypes in Jaboticabal-SP, the height and the yield were the variables that most contributed to the variability. In the the association mapping analysis, the genotypes were evaluated under conditions of high temperatures in Teresina-PI and evaluated for four traits: height of the plant at maturity, agronomic value, 100 grain weight and yield. The genotyping was carried out using the Affymetrix chip. The linkage disequilibrium between pairs of markers was calculated by the determination coefficient r2 and the association analysis between markers and the phenotype of interest was performed using the generalized linear model approach. A total of 16 significant marker-trait associations were detected for the four traits.

Orientador: José Marques Junior
Coorientador: Nivaldo de Figueiredo
Banca: Gener Tadeu Pereira
Banca: Maria Esmeralda Soares Payão Demattê
Banca: Mara de Andrade Marinho
Banca: André Luiz Gomes da Silva
Resumo: A vegetação do bioma Cerrado é predominantemente constituída por fisionomias savânicas (cerrado lato sensu). Os fatores que determinam a distribuição das diferentes fisionomias estão em constante debate, e têm sido relacionados principalmente a gradientes de fertilidade, profundidade do solo, disponibilidade hídrica, ocorrência de fogo, pressão antrópica e topografia. Entretanto, não são raros os estudos que têm encontrado resultados antagônicos ou não alinhados a esses. Neste sentido, com este estudo, pretendeu-se contribuir para o entendimento dessas questões, buscando responder às seguintes perguntas: existem diferenças florísticas e estruturais na vegetação e nos atributos do solo entre fisionomias previamente classificadas como cerradão e cerrado ralo? O cerradão ocorre em solos mais férteis e/ou com condições físicas menos restritivas que aquelas onde ocorre o cerrado ralo? Para isso, relacionamos atributos físicos e químicos do solo, assim como os padrões florísticos (riqueza e composição de espécies) e estruturais (densidade, área basal, etc.) da comunidade vegetal de três fragmentos de cerrado (cerradão, cerradão "degradado" e cerrado ralo) do nordeste do Maranhão, localizados muito próximos um do outro, em solos taxonomicamente iguais e, portanto, sujeitos a semelhantes influências ambientais e fatores pedogenéticos. Os fragmentos estudados estão inseridos num mosaico de paisagem constituído por remanescentes de áreas de cerrados em níveis diferenciados de degradação. Para as análises do solo, em cada fragmento de cerrado, foram retiradas 49 amostras do solo na camada de 0-0,10 m de profundidade, distribuídas em uma malha amostral quadrada com espaçamento regular de 15 m. As amostras foram coletadas no ponto de cruzamento entre as linhas da malha amostral. As coletas foram realizadas entre os meses de abril e maio de 2013. O levantamento fitossociológico foi realizado ...
Abstract: The vegetation of the Cerrado biome (Brazilian savanna) is predominantly composed of cerrado physiognomies (cerrado lato sensu). The determining factors on the cerrado physiognomies distribution have been continuously in question, and have been primarily related to gradients of fertility, soil depth, water availability, occurrence of fire, human disturbance and topography. However, there are few studies that have found opposing results or not aligned to these. In this sense, this study was intended to contribute to understanding of these issues, seeking to answer the following questions: Are there floristic and structural differences in vegetation and soil properties faces previously classified as cerradão and cerrado-ralo? Does cerradão occur on soils that are more fertile and/or in mild physical conditions than the cerrado-ralo? For this, we compared physical and chemical soil attributes, as well as floristic patterns (richness and species composition) and structural (density, basal area) of the plant community of three fragments of savanna (cerrado, cerrado "degradado" and cerrado-ralo) of Northeastern state of Maranhão, located very close to each other in taxonomically identical soils, and therefore subject to similar environmental influences and pedogenic factors. The studied fragments are embedded in a landscape mosaic consisting of remaining areas of cerrado in different levels of degradation. For soil analysis, 49 samples were taken in each fragment in the 0-0.10m soil profile, distributed in a square sampling grid with regular spacing of 15 m. Samples were collected at the point of intersection between the lines of the sampling grid. Sampling was carried out between the months of April and May 2013. Phytosociological survey was carried out in 49 circular plots, in which center the soil sample points were located. A sample of plant material was carried out between the months of January 2012 and May 2013. The ...
Doutor

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.
Includes bibliographical references.
Postural synergies describe characteristic patterns of actuation in human hands arising from biomechanical constraints, physical tendon coupling, and neurological control schemes. Often, a small number of synergies contain much of the information required to describe an entire human hand posture, with 80% or more of the total information encoded in only two component values. Synergies have commonly been used to identify hand shapes with minimal processing power. However, they can also be used to recreate postures in robot hands, by allowing a mechanical implementation of inter-finger coordination. This can provide benefits of reduced cost, compact size, and decreased actuator count. In this paper, a novel mechanism is proposed to drive a dexterous, versatile, 17 degree-of-freedom robot hand using only two DC motors. Posture data was collected with a dataglove, and analyzed using principal components analysis to determine the postural synergies. The synergies are then mechanically hardwired into the driving mechanism, resulting in a concept dubbed eigenpostures.
(cont.) Two eigenpostures effectively recreate the entire posture set. Several observations and suggestions are presented on tendon-drive robotic hand design in general, and also specifically targeted towards synergy- or eigenposture-based design. Avenues for further research into synergy mechanism design are proposed, including a powerful concept incorporating k-means clustering with principal components analysis to distinguish between high-precision and low-precision tasks, and greatly reduce overall error.
by Christopher Yeates Brown.
S.M.

Geochemistry has been used to determine the provenance and diagenetic history of eolian sandstone deposits. However, the grain size, sorting, cementation, and detrital composition of eolian units can change along dune foreset laminae. The purpose of this study was to test for consistent trends of compositional change along dune foresets. Such trends could increase the quality of geochemical sampling of eolian sandstones and possibly aid in estimating the original height of ancient sand dunes. XRF data was gathered for both major and trace elements from the Pennsylvanian to Permian Weber Sandstone, Early Jurassic Navajo Sandstone, and modern Coral Pink Sand Dunes of southern Utah. Data was plotted using both 2-dimensional scatter plots and 3-dimensional principal components analysis (PCA) plots. The PCA plots proved to be the most informative and suggest that there are no consistent, statistically significant geochemical trends within or between the three units sampled. However, this study found that PCA was able to show significant geochemical differences between the three units sampled, even when they are all dominated by a single mineral (>90% quartz). The Weber Sandstone had the most varied composition, and dunes within the unit could be highly dissimilar to each other. The Navajo Sandstone had less overall geochemical variability than the Weber Sandstone, and individual dunes were similar to each other. The modern Coral Pink Sand Dunes had much less compositional variation than either of the other two units, and dunes in this unit were very similar to each other.

The aim of this study is to incorporate the spectral, temporal and spatial attributes of a smoke plume for Early Forest Fire Detection. Image processing techniques are used on multispectral (red, green, blue, mid-wave infrared, and long-wave infrared) video to segment and indentify the presence of a smoke plume within a scene. The temporal and spectral variance of a smoke plume is captured through Principal Component Analysis (PCA) where the Multispectral-Multitemporal PCA is performed on a sequence of video frames simultaneously. The presence of a plume existing in one of the higher order principal components is determined by the texture of its spatial content. The texture is characterized by statistical descriptors derived from the principal component‟s joint probability density distribution of intensities occurring within a spatial relationship, known as a Gray Level Co-Occurrence Matrix (GLCM). Initial analysis is performed on selected frames where only a subset of time is considered. Once the parameters are chosen from the static analysis, the algorithms are executed on video through time to validate the method. The results show that a smoke plume is readily segmented via PCA. Based on the five spectral bands over 3 seconds sampled at 1 second, the plume exists in the 7th principal component. Within these principal components, the smoke‟s presence is best identified by the correlation texture descriptor. The smoke is very spatially correlated compared to the scene at large. Therefore a spike in the spatial correlation of the principal components is all that is needed to identify the start of the smoke plume.

Dissertation (PhD)--Stellenbosch University, 2001.
ENGLISH ABSTRACT: Gower and Hand offer a new perspective on the traditional biplot. This perspective provides a unified approach to principal component analysis (PCA) biplots based on Pythagorean distance; canonical variate analysis (CVA) biplots based on Mahalanobis distance; non-linear biplots based on Euclidean embeddable distances as well as generalised biplots for use with both continuous and categorical variables. The biplot methodology of Gower and Hand is extended and applied in statistical discrimination and classification. This leads to discriminant analysis by means of PCA biplots, CVA biplots, non-linear biplots as well as generalised biplots. Properties of these techniques are derived in detail. Classification regions defined for linear discriminant analysis (LDA) are applied in the CVA biplot leading to discriminant analysis using biplot methodology. Situations where the assumptions of LDA are not met are considered and various existing alternative discriminant analysis procedures are formulated in terms of biplots and apart from PCA biplots, QDA, FDA and DSM biplots are defined, constructed and their usage illustrated. It is demonstrated that biplot methodology naturally provides for managing categorical and continuous variables simultaneously. It is shown through a simulation study that the techniques based on biplot methodology can be applied successfully to the reversal problem with categorical variables in discriminant analysis. Situations occurring in practice where existing discriminant analysis procedures based on distances from means fail are considered. After discussing self-consistency and principal curves (a form of non-parametric principal components), discriminant analysis based on distances from principal curves (a form of a conditional mean) are proposed. This biplot classification procedure based upon principal curves, yields much better results. Bootstrapping is considered as a means of describing variability in biplots. Variability in samples as well as of axes in biplot displays receives attention. Bootstrap a-regions are defined and the ability of these regions to describe biplot variability and to detect outliers is demonstrated. Robust PCA and CVA biplots restricting the role of influential observations on biplot displays are also considered. An extensive library of S-PLUS computer programmes is provided for implementing the various discriminant analysis techniques that were developed using biplot methodology. The application of the above theoretical developments and computer software is illustrated by analysing real-life data sets. Biplots are used to investigate the degree of capital intensity of companies and to serve as an aid in risk management of a financial institution. A particular application of the PCA biplot is the TQI biplot used in industry to determine the degree to which manufactured items comply with multidimensional specifications. A further interesting application is to determine whether an Old-Cape furniture item is manufactured of stinkwood or embuia. A data set provided by the Western Cape Nature Conservation Board consisting of measurements of tortoises from the species Homopus areolatus is analysed by means of biplot methodology to determine if morphological differences exist among tortoises from different geographical regions. Allometric considerations need to be taken into account and the resulting small sample sizes in some subgroups severely limit the use of conventional statistical procedures. Biplot methodology is also applied to classification in a diabetes data set illustrating the combined advantage of using classification with principal curves in a robust biplot or biplot classification where covariance matrices are unequal. A discriminant analysis problem where foraging behaviour of deer might eventually result in a change in the dominant plant species is used to illustrate biplot classification of data sets containing both continuous and categorical variables. As an example of the use of biplots with large data sets a data set consisting of 16828 lemons is analysed using biplot methodology to investigate differences in fruit from various areas of production, cultivars and rootstocks. The proposed a-bags also provide a measure of quantifying the graphical overlap among classes. This method is successfully applied in a multidimensional socio-economical data set to quantify the degree of overlap among different race groups. The application of the proposed biplot methodology in practice has an important byproduct: It provides the impetus for many a new idea, e.g. applying a peA biplot in industry led to the development of quality regions; a-bags were constructed to represent thousands of observations in the lemons data set, in tum leading to means for quantifying the degree of overlap. This illustrates the enormous flexibility of biplots - biplot methodology provides an infrastructure for many novelties when applied in practice.
AFRIKAANSE OPSOMMING: Gower en Hand bied 'n nuwe perspektief op die tradisionele bistipping. Hierdie perspektief verskaf 'n uniforme benadering tot hoofkomponent analise (HKA) bistippings gebaseer op Pythagoras-afstand; kanoniese veranderlike analise (KVA) bistippings gebaseer op Mahalanobis-afstand; nie-lineere bistippings gebaseer op Euclidies inbedbare afstande sowel as veralgemeende bistippings vir gebruik wanneer beide kontinue en kategoriese veranderlikes voorkom. Die bistippingsmetodologie van Gower en Hand word uitgebrei en toegepas in statistiese diskriminasie en klassifikasie. Dit lei tot diskriminantanalise met behulp van HKA bistippings, KVA bistippings, nie-lineere bistippings sowel as veralgemeende bistippings. Die eienskappe van hierdie tegnieke word in besonderhede afgelei. Die toepassing van die konsep van 'n klassifikasiegebied in die KVA bistipping baan die weg vir lineere diskriminantanalise (LDA) met behulp van bistippingsmetodologie. Situasies waar daar nie aan die aannames van LDA voldoen word nie kry aandag en verskeie bestaande altematiewe diskriminantanalise prosedures word in terme van bistippings geformuleer en naas HKA bistippings, word QDA, FDA en DSM bistippings gedefinieer, gekonstrueer en hul gebruike gedemonstreer. Dit word aangetoon dat bistippingsmetodologie op 'n natuurlik wyse voorsiening maak om kategoriese veranderlikes en kontinue veranderlikes gelyktydig te hanteer. Daar word met behulp van 'n simulasie-studie aangetoon dat tegnieke gebaseer op die bistippingsmetodologie wat ontwikkel IS, suksesvol by die sogenaamde ornkeringsprobleem by diskriminantanalise met kategoriese veranderlikes gebruik kan word. Verder word aangevoer dat daar baie praktiese situasies voorkom waar bestaande prosedures van diskriminantanalise faal omdat dit op afstande vanaf gemiddeldes gebaseer IS. Na 'n bespreking van self-konsekwentheid en hoofkrommes ('n vorm van nieparametriese hoofkomponente) word voorgestel om diskriminantanalise op afstand vanaf hoofkrommes ('n vonn van 'n voorwaardelike gemiddelde) te baseer. Sodoende is 'n bistippingklassifikasie prosedure wat op afstand vanaf hoofkrommes gebaseer is en wat baie beter resultate lewer, ontwikkel. Die variasie in die posisies van datapunte in die bistipping sowel as van die bistippingsasse word bestudeer met behulp van skoenlusmetodes. 'n Skoenlus a-gebied word gedefinieer en dit word gedemonstreer hoe so 'n a-gebied aangewend kan word om variasie in bistippings te beskryf en wegleers te identifiseer. Robuuste HKA en KV A bistippings wat die rol van invloedryke waamemings op die bistipping beperk, word bespreek. 'n Omvangryke biblioteek van S-PLUS rekenaarprogramme is geskryf VIr die implementering van die verskillende diskriminantanalise tegnieke wat met behulp van bistippingsmetodologie ontwikkel is. Die toepassing van die voorafgaande teoretiese ontwikkelinge en rekenaarprogramme word geillustreer aan die hand van werklike datastelle vanuit die praktyk. So word bistippings gebruik om die mate van kapitaalintensiteit van ondememings te ondersoek en om as hulpmiddel by risikobestuur van 'n finansiele instelling te dien. 'n Besondere toepassing van die HKA bistipping is die TQI bistipping wat in die industriele omgewing gebruik word ten einde te bepaal tot watter mate vervaardigde artikels aan neergelegde meerdimensionele spesifikasies voldoen. 'n Verdere interessante toepassing is om te bepaal of 'n Ou-Kaapse meubelstuk van stinkhout of embuia gemaak is. 'n Datastel verskaf deur Wes-Kaap Natuurbewaring in verband met die bekende padloper skilpad, Homopus areolatus, is met behulp van bistippings geanaliseer om te bepaal of daar morfometriese verskille tussen die padlopers afkomstig van bepaalde geografiese gebiede is. Allometriese beginsels moes ook in ag gene em word en die min waamemings in sommige van die subgroepe het tot gevolg dat konvensionele statistiese tegnieke nie sonder meer gebruik kan word nie. Die bistippingsmetodologie is ook toegepas op klassifikasie by 'n diabetes datastel om die gekombineerde gebruik van. hoofkrommes in 'n robuuste bistipping te illustreer en bistippingklassifikasie waar daar sprake van ongelyke kovariansiematrikse is. 'n Diskriminantanalise probleem waar die weidingsvoorkeure van wildsbokke 'n verandering in die dominante plantegroei tot gevolg kan he, word gebruik om bistippingklassifikasie met data waar kontinue sowel as kategoriese veranderlikes verskaf word, te illustreer. As voorbeeld van die gebruik van bistippings by 'n groot datastel is 'n datastel bestaande uit waamemings van 16828 suurlemoene met behulp van bistippingsmetodologie geanaliseer ten einde verskille in vrugte afkomstig van verskillende produsente-streke, kultivars en onderstamme te ondersoek. Die a-sakkies wat hier ontwikkel is, lei tot kwantifisering van die grafiese oorvleueling van groepe. Hierdie beginsel word suksesvol toegepas in 'n meerdimensionele sosio-ekonomiese datastel om die mate van oorvleueling van verskillende bevolkingsgroepe te kwantifiseer. Die toepassing van die voorgestelde bistippingsmetodologie in die praktyk lei tot 'n belangrike newe-produk: Dit verskaf die stimulus tot die ontstaan van nuwe idees, byvoorbeeld, die toepassing van 'n HKA bistipping in 'n industriele omgewing het tot die ontwikkeling van die konsep van 'n kwaliteitsgebied aanleiding gegee; a-sakkies is gekonstrueer om duisende waamemings in die suurlemoendatastel te verteenwoordig wat weer gelei het tot 'n metode om die graad van oorvleueling te kwantifiseer. Hierdeur is die geweldige veelsydigheid van bistippings geillustreer - bistippingsmetodologie verskaf die infrastruktuur vir baie vindingryke toepassings in die praktyk.

With an increasing number of students enrolling at higher education institutions in South Africa, it has become important to investigate whether these institutions are using their resources adequately. This study uses data envelopment analysis (DEA) to estimate the efficiency of 23 South African tertiary education institutions based on both teaching and research outputs. Using DEA we are able to rank South African universities according to their use of resources in these two areas. These rankings can identify institutions which are performing well and also those which require improvement. The effect that merging institutions has on this efficiency is also determined. Owing to the limited sample size, variable reduction techniques, including the efficiency contribution measure (ECM) and principal components analysis (PCA-DEA), were used to improve the discrimination of the analysis.