Risk measurement of mortgage-backed security portfolios presents a very involved task for analysts and portfolio managers of such investments. A strong predictive econometric model that can account for the variability of these securities in the future would prove a very useful tool for anyone in this financial market sector due to the difficulty of evaluating the risk of mortgage cash flows and prepayment options at the same time. This project presents two linear regression methods that attempt to explain the risk within these portfolios. The first study involves a principal components analysis on absolute changes in market data to form new sets of uncorrelated variables based on the variability of original data. These principal components then serve as the predictor variables in a principal components regression, where the response variables are the day-to-day changes in the net asset values of three agency mortgage-backed security mutual funds. The independence of each principal component would allow an analyst to reduce the number of observable sets in capturing the risk of these portfolios of fixed income instruments. The second idea revolves around a simple ordinary least squares regression of the three mortgage funds on the sets of the changes in original daily, weekly and monthly variables. While the correlation among such predictor variables may be very high, the simplicity of utilizing observable market variables is a clear advantage. The goal of either method was to capture the largest amount of variance in the mortgage-backed portfolios through these econometric models. The main purpose was to reduce the residual variance to less than 10 percent, or to produce at least 90 percent explanatory power of the original fund variances. The remaining risk could then be attributed to the nonlinear dependence in the changes in these net asset values on the explanatory variables. The primary cause of this nonlinearity is due to the prepayment put option inherent in these securities.

We consider a principal components based decomposition of the expected value of the multivariate quadratic loss function, i.e., MQL. The principal components are formed by scaling the original data by the contents of the loss constant matrix, which defines the economic penalty associated with specific variables being off their desired target values. We demonstrate the extent to which a subset of these ``loss-scaled principal components", i.e., LSPC, accounts for the two components of expected MQL, namely the trace-covariance term and the off-target vector product. We employ the LSPC to solve a robust design problem of full and reduced dimensionality with deterministic models that approximate the true solution and demonstrate comparable results in less computational time. We also employ the LSPC to construct a test statistic called loss-scaled T^2 for multivariate statistical process control. We show for one case how the proposed test statistic has faster detection than Hotelling's T^2 of shifts in location for variables with high weighting in the MQL. In addition we introduce a principal component based decomposition of Hotelling's T^2 to diagnose the variables responsible for driving the location and/or dispersion of a subgroup of multivariate observations out of statistical control. We demonstrate the accuracy of this diagnostic technique on a data set from the literature and show its potential for diagnosing the loss-scaled T^2 statistic as well.

Μελέτη των ατμοσφαιρικών ρύπων στην πόλη της Πάτρας για το έτος 2010 με τη μέθοδο της ανάλυσης σε κύριες συνιστώσες και κάνοντας χρήση του στατιστικού πακέτου Minitab16 / Research on air pollutants in the city of Patras for the year 2010 using the method of Principal Components Analysis. The results are elaborated using the statistical program minitab16.

This thesis consists of work conducted in two separate studies, evaluating the performance of passive systems for treating wastewater effluents. The first study involved the characterization of three wastewater stabilization ponds (WSPs) providing secondary and tertiary treatment for municipal wastewater at a facility in Amherstview, Ontario, Canada. Since 2003, the WSPs have experienced excessive algae growth and high pH levels during the summer months. A full range of parameters consisting of: pH, chlorophyll-a (chl-a), dissolved oxygen (DO), temperature, alkalinity, oxidation-reduction potential (ORP), conductivity, nutrient species, and organic matter measures; were monitored for the system and the chemical dynamics in the three WSPs were assessed through multivariate statistical analysis. Supplementary continuous monitoring of pH, chl-a, and DO was performed to identify time-series dependencies. The analyses showed strong correlations between chl-a and sunlight, temperature, organic matter, and nutrients, and strong time dependent correlations between chl-a and DO and between chl-a and pH. Additionally, algae samples were collected and analyzed using metagenomics methods to determine the distribution and speciation of algae growth in the WSPs. A strong shift from the dominance of a major class of green algae, chlorophyceae, in the first WSP, to the dominance of land plants, embryophyta – including aquatic macrophytes – in the third WSP, was observed and corresponded to field observations during the study period. The second study involved the evaluation of the performance and chemical dynamics of a hybrid-passive system treating leachate from a municipal solid waste (MSW) landfill in North Bay, Ontario, Canada. Over a three year period, monitoring of a full range of parameters consisting of: pH, DO, temperature, alkalinity, ORP, conductivity, sulfate, chloride, phenols, solids fractions, nutrient species, organic matter measures, and metals; was conducted bi-weekly and the dataset was analyzed with time series and multivariate statistical techniques. Regression analyses identified 8 parameters that were most frequently retained for modelling the five criteria parameters (alkalinity, ammonia, chemical oxygen demand, iron, and heavy metals), on a statistically significant level (p < 0.05): conductivity, DO, nitrite, organic nitrogen, ORP, pH, sulfate, and total volatile solids. / Thesis (Master, Civil Engineering) -- Queen's University, 2013-10-27 05:29:20.564

Multicollinearity can be present in the propensity score model when estimating average treatment effects (ATEs). In this thesis, logistic ridge regression (LRR) and principal components logistic regression (PCLR) are evaluated as an alternative to ML estimation of the propensity score model. ATE estimators based on weighting (IPW), matching and stratification are assessed in a Monte Carlo simulation study to evaluate LRR and PCLR. Further, an empirical example of using LRR and PCLR on real data under multicollinearity is provided. Results from the simulation study reveal that under multicollinearity and in small samples, the use of LRR reduces bias in the matching estimator, compared to ML. In large samples PCLR yields lowest bias, and typically was found to have the lowest MSE in all estimators. PCLR matched ML in bias under IPW estimation and in some cases had lower bias. The stratification estimator was heavily biased compared to matching and IPW but both bias and MSE improved as PCLR was applied, and for some cases under LRR. The specification with PCLR in the empirical example was usually most sensitive as a strongly correlated covariate was included in the propensity score model.

Batch effects are due to probe-specific systematic variation between groups of samples (batches) resulting from experimental features that are not of biological interest. Principal components analysis (PCA) is commonly used as a visual tool to determine whether batch effects exist after applying a global normalization method. However, PCA yields linear combinations of the variables that contribute maximum variance and thus will not necessarily detect batch effects if they are not the largest source of variability in the data. We present an extension of principal components analysis to quantify the existence of batch effects, called guided PCA (gPCA). We describe a test statistic that uses gPCA to test if a batch effect exists. We apply our proposed test statistic derived using gPCA to simulated data and to two copy number variation case studies: the first study consisted of 614 samples from a breast cancer family study using Illumina Human 660 bead-chip arrays whereas the second case study consisted of 703 samples from a family blood pressure study that used Affymetrix SNP Array 6.0. We demonstrate that our statistic has good statistical properties and is able to identify significant batch effects in two copy number variation case studies. We further compare existing batch effect correction methods and apply gPCA to test their effectiveness. We conclude that our novel statistic that utilizes guided principal components analysis to identify whether batch effects exist in high-throughput genomic data is effective. Although our examples pertain to copy number data, gPCA is general and can be used on other data types as well.

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) / Doutor

Orientador: Carlos Roberto Padovani / Banca: Adalberto José Crocci / Banca: Luiz Gonzaga Manzine / Resumo: Os experimentos agronômicos, em geral, apresentam uma quantidade razoável de variáveis observadas e uma complexa estrutura de variação entre e dentro dessas variáveis. Essa estrutura de variação acarreta uma dificuldade para a utilização dos procedimentos requeridos pelo modelo estatístico, em virtude do difícil acesso a programas computacionais para a análise dos dados multivariados. Uma alternativa para redimensionar a quantidade de variáveis consiste na técnica dos componentes principais, que consegue descrever um conjunto com um número menor de variáveis não correlacionadas entre si, ordenadas de maneira decrescente pelas magnitudes das variâncias, de tal forma que a variância total do conjunto inicial seja preservada. Em síntese, a prática da análise de componentes principais é considerada sob o objetivo da redução do espaço paramétrico. Uma das dificuldades encontrada pelos pesquisadores no uso da técnica dos componentes principais, consiste na determinação do número de componentes que deve ser utilizado na redução do espaço paramétrico. Dentre alguns métodos exploratórios discutidos foram apresentados quatro critérios para a escolha do número de componentes principais os quais retem de forma qualificada, a informação contida nas variáveis originais. Neste sentido, foi proposto no presente estudo, a elaboração de um programa computacional, desenvolvido em linguagem MAPLE V.3 e CLIPPER 5.1, de fácil manuseio e acessível a todos os pesquisadores das áreas agronômicas. Visando a operacionalização do aplicativo e a utilização dos procedimentos de análise multivariada, finalizou-se o estudo apresentando dois exemplos envolvendo situações observadas na literatura agronômica, onde no primeiro faz-se uma abordagem pela metodologia univariada e pela utilização de componentes principais por processo gráfico, e no segundo... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The agronomical experiments, in general, introduce a reasonable quantity of observed variables and a variation complex structure between and within these variables. This variation structure carries a difficulty for the utilization of the procedures required by the statistical model, in view of the difficult access for computational programs for the analysis of the multivariate data. An option for redimensionate the quantity of variable consists in the technique of the principal components, which manages to describe a set with a smaller number of variable not correlated to each other, ordenate of decreasing way by the magnitudes of the variances, of such a form that the total variance of the initial set be preserved. In synthesis, the practice of the analysis of principal components is considered under the objective of the reduction of the parametric space. One of the difficulties found by the researchers in the use of the technique of the principal components, it consists in the determination of the number of components that should be used in the reduction of the parametric space. Among some argued exploratory methods were introduced four criteria for the choice of the number of principal components the ones retain of form qualified, the information contained in the original variables. In this sense, it was proposed at study present, the elaboration of a computational program, developed in language MAPLE V.3 and CLIPPER 5.1, of easy handling and accessible to all the researchers of the agronomical areas. Aiming at operationalization of the application and the utilization of the multivariate analysis procedures, it was concluded the study introducing two examples involving situations observed in the agronomical literature, where in the first an approach is done by the univariate methodology and by the utilization of principal components for prosecute graph, and in the second... (Complete abstract click electronic access below) / Mestre

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 Roberto Môro / Banca: João Carlos de Oliveira / Banca: Antonio Sérgio Ferraudo / Banca: José Antonio Alberto da Silva / Banca: Jair Costa Nachtigal / Resumo: A pupunheira tem um potencial econômico e social muito grande, sendo a palmeira mais importante na América pré-colombiana, constituindo junto com o milho e a mandioca, a base da alimentação dos povos primitivos. Os principais produtos extraídos são o palmito e os frutos para o consumo humano direto, alimento animal, farinhas para consumo humano e óleo vegetal. Os objetivos do presente trabalho foram de utilizar uma lista de descritores morfológicos recomendada, para discriminar primeiramente as raças Pará e Putumayo e após sua validação estatística, verificar também a existência da raça Solimões, que até hoje tem sido negada. Foram aplicadas técnicas estatísticas univariadas e multivariadas na tentativa de discriminar as raças. Dos 42 descritores iniciais, 25 apresentaram diferenças significativas entre as raças e 15 tiveram aproximação normal. A análise discriminante mostrou que a raça Pará possuía 15% das plantas mal classificadas e Putumayo 14%, já com a seleção de desenvolmer para componentes principais, as percentagens foram 9 e 19%, respectivamente, para as duas raças. A população de Manacapuru, não formou grupo nas duas primeiras análises de agrupamento e nem com componentes principais. As três análises em conjunto, conseguiram discriminar as raças Pará, Putumayo e Solimões, sendo os descritores mais importantes nesta discriminação e classificação das raças: número de espigas por cacho, comprimento da ráquis, peso dos frutos, espessura das cascas, facilidade para descascar os frutos, peso das cascas, sabor dos frutos, espessura da polpa, distância morfológica dos frutos e peso das sementes. / Abstract: The peach palm has a economic and social potential very great being the palm most important in the América pre-Colombian, contribuiting together with the maize and the cassava in the indenous feeds. The target of the present work was: to use a morphological descriptor list recommended, to discriminate between two landraces and descriptors validation , to verify the existence of solimoes landraces. Univariated and multivariated statistical techniques were used to attemp discriminate the landraces. Form fort yone initial descriptors, twenty five had presented significant difference between the landraces and fifteen had presented normal approach. The discriminant analysis have showed that Pará landrace possessed fifteen percent of the plant badly c1assified and Putumayo about fourteen percent to it. In the analysis of principal component, the percentages were nine and nineteen percent, respectively, for the two landraces. Manacapuru population did not form c1usterin in the two first one analysis of and nor with principal components. Three joint analysis in the set had obtained to discriminate the Pará, Putumayo and Solimoes landraces and the discrimnant analysis with three landraces, c1assified Manacapuru of the Putumayo landrace inside. The most important descriptors in the discrimination between landraces were: numbers of ears per raceme, raquis length, fruit weight, thickness of fruits bark, facility to peel fruits, weight of fruit bark, fruit flavor, pulp thickness, morphological distance between fruits and seed weight. / Doutor