Tesi: "Principal components"

1

Nunes, Madalena Baioa Paraíso. "Portfolio selection : a study using principal component analysis." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14598.

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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

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2

Kassaye, Meseret Haile, and Yigit Demir. "Calibration Based On Principal Components." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-26582.

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This study is concerned in reducing high dimensionality problem of auxiliary variables in the calibration estimation with the presence of nonresponse. The calibration estimation is a weighting method assists to compensate for the nonresponse in the survey analysis. Calibration estimation using principal components (PCs) is new idea in the literatures. Principal component analysis (PCA) is used in reduction dimension of the auxiliary variables. PCA in calibration estimation is presented as an alternative method for choosing the auxiliary variables. In this study, simulation on the real data is used and nonresponse mechanism is applied on the sampled data. The calibration estimator is compared using different criteria such as varying the nonresponse rate and increasing the sample size. From the results, although the calibration estimation based on the principal components have reasonable outputs to use instead of the whole auxiliary variables for the means, the variance is very large compared with based on original auxiliary variables. Finally, we identified the principal component analysis is not efficient in the reduction of high dimensionality problem of auxiliary variables in the calibration estimation for large sample sizes.

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3

Sun, Linjuan. "Simple principal components." Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.429528.

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4

Duras, Toni. "Aspects of common principal components." Licentiate thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-38587.

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The focus of this thesis is the common principal component (CPC) model, the generalization of principal components to several populations. Common principal components refer to a group of multidimensional datasets such that their inner products share the same eigenvectors and are therefore simultaneously diagonalized by a common decorrelator matrix. Common principal component analysis is essentially applied in the same areas and analysis as its one-population counterpart. The generalization to multiple populations comes at the cost of being more mathematically involved, and many problems in the area remains to be solved. This thesis consists of three individual papers and an introduction chapter.In the first paper, the performance of two different estimation methods of the CPC model is compared for two real-world datasets and in a Monte Carlo simulation study. The second papers show that the orthogonal group and the Haar measure on this group plays an important role in PCA, both in single- and multi-population principal component analysis. The last paper considers using common principal component analysis as a tool for imposing restrictions on system-wise regression models. When the exogenous variables of a multi-dimensional model share common principal components, then each of the marginal models in the system is, up to their eigenvalues, identical. They henceform a class of regression models situated in between the classical seemingly unrelated regressions, where each set of explanatory variables is unique, and multivariate regression, where each marginal model shares the same common set of regressors.

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5

Brubaker, S. Charles. "Extensions of principal components analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29645.

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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.

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6

Khwambala, Patricia Helen. "The importance of selecting the optimal number of principal components for fault detection using principal component analysis." Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/11930.

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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.

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7

Zavala, Frank Alcorta. "Principals' Perceptions of the Most Important Components in an Effective Principal Preparation Program." ScholarWorks, 2014. https://scholarworks.waldenu.edu/dissertations/26.

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Researchers in educational leadership have identified a need to improve principal preparation programs to meet today's educational demands. According to school administrators in the local area, not all leadership preparation programs used the same pedagogies to prepare future leaders, and principals were critical of existing leadership practices. School districts, students, parents, and community stakeholders would benefit from well-prepared administrators who can apply the most effective habits of principalship. The conceptual framework of the study was derived from J. Davis and Jazzar's 7 habits of an effective principal preparation program. For this qualitative case study, 16 principals were interviewed to find out which components of a principal preparation program they thought were the most important or had best prepared them for their positions. Analysis involved open coding, and resulting themes revealed that principals perceived the most important components to be a multisituational internship and extensive experience with school budget/finance. A professional development session was created to share interview responses with policymakers. Principal preparation programs that involve an in-depth internship and practice with school budget and finance could be used to assist policy makers in developing leadership training programs for future principals to improve student and school performance for school districts. This project study could foster social change with greater school success for students, resulting from improvement in leadership preparation programs.

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8

Brandenberg, Romano Rodolfo. "Principal Components Analysis of Commodity Trading Advisors." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02604577002/$FILE/02604577002.pdf.

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9

Uddin, Mudassir. "Interpretation of results from simplified principal components." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301216.

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Linear multivariate statistical methods are widely used for analysing data sets which consist of a large number of variables. These techniques, which include principal component analysis, factor analysis, canonical correlation analysis, redundancy analysis and discriminant analysis, all produce a set of new variables, commonly called 'factors', according to some criterion which differs for different techniques. Among these techniques, principal component analysis is one of the most popular techniques used for reducing the dimensions of the multivariate data set. In many applications, when Principal Component Analysis (PCA) is performed on a large number of variables, the interpretation of the results is not simple. The derived eigenvectors of the sample covariance or correlation matrix are not necessarily in a simple form, with all coefficients either 'large' or 'negligible'. To aid interpretation, it is fairly common practice to rotate the retained set of components, often using orthogonal rotation. The purpose of rotation is to simplify structure, and thus to make it easier to interpret the low-dimensional space represented by the retained set of components. Thus, quantification of simplicity is a two step process. The first set involves the extraction of the feature from the data called components, while the second stage uses a rotation method to simplify the structure. One of the two main purposes of this thesis is to combine into one step these two separate stages of dimension reduction (finding the components) and simplification (rotation). This goal is achieved by combining these two objectives in the form of a single function leading to what we call Simplified Components (SCs). Another objective is to discover which of the many possible criteria suggested in factor analysis can be adopted in the proposed procedure of SCs. Thus, a simplified one-step procedure of SCs is proposed, using four measures of simplicity, namely varimax, quartimax, orthomax and equamax indices.

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10

Neuenschwander, Beat. "Common principal components for dependent random vectors /." [Bern] : [s.n.], 1991. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.

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11

Gu, Hong, and 谷紅. "Influence diagnostics in principal components and canonical analyses." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31238518.

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12

Pepler, Pieter Theo. "The identification and application of common principal components." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96101.

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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.

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López, Alfageme Alfredo Ignacio. "Nonlinear principal components analysis for measures and images." Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/114861.

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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.

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14

Bhamani, Feroz. "Hedging Interest-Rate Options Using Principal Components Analysis." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29250.

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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.

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Murphy, Terrence Edward. "Multivariate Quality Control Using Loss-Scaled Principal Components." Diss., Available online, Georgia Institute of Technology, 2004:, 2004. http://etd.gatech.edu/theses/available/etd-11222004-122326/unrestricted/murphy%5Fterrence%5Fe%5F200412%5Fphd.pdf.

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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.

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Fountanas, Leonidas. "Principal components based techniques for hyperspectral image data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Dec%5FFountanas.pdf.

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Fučík, Vojtěch. "Principal component analysis in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.

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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.

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Patak, Zdenek. "Robust principal component analysis via projection pursuit." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29737.

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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

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Brennan, Victor L. "Principal component analysis with multiresolution." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.

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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).

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Ganey, Raeesa. "Biplots based on principal surfaces." Doctoral thesis, Faculty of Science, 2019. https://hdl.handle.net/11427/31695.

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Principal surfaces are smooth two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of the data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. Biplots are considered as extensions of the ordinary scatterplot by providing for more than three variables. When the difference between data points are measured using a Euclidean embeddable dissimilarity function, observations and the associated variables can be displayed on a nonlinear biplot. A nonlinear biplot is predictive if information on variables is added in such a way that it allows the values of the variables to be estimated for points in the biplot. Prediction trajectories, which tend to be nonlinear are created on the biplot to allow information about variables to be estimated. The goal is to extend the idea of nonlinear biplot methodology onto principal surfaces. The ultimate emphasis is on high dimensional data where the nonlinear biplot based on a principal surface allows for visualisation of samples, variable trajectories and predictive sets of contour lines. The proposed biplot provides more accurate predictions, with an additional feature of visualising the extent of nonlinearity that exists in the data.

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Vargas-Guzman, Jose Antonio 1961. "Scaling variances, correlation and principal components with multivariate geostatistics." Diss., The University of Arizona, 1998. http://hdl.handle.net/10150/282813.

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A new concept of dispersion (cross) covariance has been introduced for the modeling of spatial scale dependent multivariate correlations. Such correlations between attributes depend on the spatial size of the domain and size of samples in the population and have been modeled by first time in this research. Modeled correlations have been used to introduce a new scale dependent principal component analysis (PCA) method. This method is based on computation of eigen values and vectors from dispersion covariance matrices or scale dependent correlations which can be modeled from integrals of matrix variograms. For second order stationary random functions this PCA converges for large domains to the classic PCA. A new technique for computing variograms from spatial variances have also been developed using derivatives. For completeness, a deeper analysis of the linear model of coregionalizations widely used in multivariate geostatistics has been included as well. This last part leads to a new more sophisticated model we termed "linear combinations coregionalization model." This whole research explains the relationship between different average states and the micro- state of vector random functions in the framework of geostatistics. Examples have been added to illustrate the practical application of the theory. This approach will be useful in all earth sciences and particularly in soil and environmental sciences.

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Burka, Zak. "Perceptual audio classification using principal component analysis /." Online version of thesis, 2010. http://hdl.handle.net/1850/12247.

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Wedlake, Ryan Stuart. "Robust principal component analysis biplots." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.

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Silva, Norberto da [UNESP]. "Aplicativo computacional para utilização de componentes principais em experimentação agronômica." Universidade Estadual Paulista (UNESP), 2005. http://hdl.handle.net/11449/90718.

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Made available in DSpace on 2014-06-11T19:24:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-09-08Bitstream added on 2014-06-13T18:52:36Z : No. of bitstreams: 1 silva_nr_me_botfca.pdf: 603039 bytes, checksum: 4acd5387f9aeb3ee2adb6a3405b329e6 (MD5) 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... 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)

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Lloret, Felipe Cyrillo 1988. "Pterodon pubescens Benth. : influência da exposição de frutos ao calor no teor de vouacapanos sobre a atividade antiproliferativa in vitro." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/288222.

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Orientador: Mary Ann Foglio Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Odontologia de Piracicaba Made available in DSpace on 2018-08-26T10:27:31Z (GMT). No. of bitstreams: 1 Lloret_FelipeCyrillo_M.pdf: 3758612 bytes, checksum: 7002aac1760fd55833ce91a085d63599 (MD5) Previous issue date: 2014 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

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Vargas, Giovana. "A genomic association and prediction of principal components of growth traits and visual scores in Nellore cattle /." Jaboticabal, 2018. http://hdl.handle.net/11449/180489.

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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

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Saad, Maarouf. "Application of principal components analysis to long-term reservoir management." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=75910.

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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.

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Zayed, Mohammad Abd-Allateef. "Curve estimation based on localised principal components : theory and applications." Thesis, Durham University, 2011. http://etheses.dur.ac.uk/3330/.

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In this work, basic theory and some proposed developments to localised principal components and curves are introduced. In addition, some areas of application for local principal curves are explored. Only relatively recently, localised principal components utilising kernel-type weights have found their way into the statistical literature. In this study, the asymptotic behaviour of the method is investigated and extended to the context of local principal curves, where the characteristics of the points at which the curve stops at the edges are identified. This is used to develop a method that lets the curve `delay' convergence if desired, gaining more access to boundary regions of the data. Also, a method for automatic choice of the starting point to be one of the local modes within the data cloud is originated. The modified local principal curves' algorithm is then used for fitting multi-dimensional econometric data. Special attention is given to the role of the curve parametrisation, which serves as a feature extractor and also as a prediction tool when properly linked to time as a probable underlying latent variable. Local principal curves provide a good dimensionality reduction and feature extraction tool for insurance industry key indicators and consumer price indices. Also, through `calibrating' it with time, curve parametrisation is used for the purpose of predicting unemployment and inflation rates.

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Lee, Wonwoo. "Fractional principal components regression: a general approach to biased estimators." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/49819.

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Cheng, Tze-shan. "Trend forecasting of tropical cyclone behaviour using Eigenvector analysis of the relationship with 500 hPa pattern /." [Hong Kong : University of Hong Kong], 1988. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12354636.

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Griffin, Kent E. "Solving the principal minor assignment problem and related computations." Online access for everyone, 2006. http://www.dissertations.wsu.edu/Dissertations/Summer2006/k%5Fgriffin%5F061406.pdf.

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Shannak, Kamal Majed. "On Non-Linear Principal Component Analysis for Process Monitoring." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/ShannakKM2004.pdf.

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Liu, Junshi, Caleb D. Bazyler, Christopher B. Taber, Tony Pustina, and Satoshi Mizuguchi. "Application of Principal Components Analysis in Kinetics Study for Isometric Squat." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etsu-works/3839.

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Martins, Fredgardson Costa [UNESP]. "Relação solo-vegetação em área de cerrado no nordeste do Maranhão, Brasil." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/121985.

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Made available in DSpace on 2015-04-09T12:28:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-09-18Bitstream added on 2015-04-09T12:47:54Z : No. of bitstreams: 1 000812398.pdf: 1324699 bytes, checksum: d0d715f59d8c26b64d6a305766066855 (MD5) 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 ...

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Sousa, Camila Campêlo de. "Mapeamento associativo para tolerância a altas temperaturas em germoplasma exótico de soja (Glycine max)." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11137/tde-08012016-092538/.

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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.

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Martins, Fredgardson Costa. "Relação solo-vegetação em área de cerrado no nordeste do Maranhão, Brasil /." Jaboticabal, 2014. http://hdl.handle.net/11449/121985.

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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

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Cao, Zisheng, and 曹子晟. "Incremental algorithms for multilinear principal component analysis of tensor objects." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/208151.

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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

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Allemang, Matthew R. "Comparison of Automotive Structures Using Transmissibility Functions and Principal Component Analysis." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367944783.

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Le, Hanh T. Banking &amp Finance Australian School of Business UNSW. "Discrete PCA: an application to corporate governance research." Awarded by:University of New South Wales. Banking & Finance, 2007. http://handle.unsw.edu.au/1959.4/40753.

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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.

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Zeng, Yicheng. "Order determination for large matrices with spiked structure." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/667.

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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.

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Ho, Chi-wing Daniel. "An analysis of property-specific quality attributes for office buildings /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21979315.

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Morris, Annie J. "Geospatial and statistical foundations for streamflow synthesis in West Virginia." Morgantown, W. Va. : [West Virginia University Libraries], 2002. http://etd.wvu.edu/templates/showETD.cfm?recnum=2738.

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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).

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Hissanaga, Hugo Mamoru Aoki. "Previsão da curva de juros com análise de componentes principais utilizando matriz de covariâcia de longo prazo." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-27102017-102841/.

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Apesar da Análise de Componentes Principais (PCA) ser um dos métodos mais importantes na análise da estrutura a termo de taxa de juros, há fortes indícios de não ser adequada para estimar fatores da curva de juros quando há presença de dependência temporal e erros de medida. Para corrigir esses problemas é indicado utilizar a matriz de covariância de longo prazo, extraindo a correta estrutura de covariância presente nestes processos. Neste trabalho, mostramos que realizar a previsão fora da amostra da curva de taxa de juros com o método de Análise de Componentes Principais (PCA) utilizando como base a matriz de covarância de longo prazo (LRCM) parece ser mais acurada comparada a PCA com base na matriz de covariância amostral. Although Principal Component Analysis (PCA) is one of the most common methods to estimate the structure of interest rate volatility, there are strong indications that it is not adequate to estimate interest rate factors when there is temporal dependence and measurement errors. To correct these problems it is necessary to use the longterm covariance matrix, to extract the correct covariance structure present in these processes. In this work, we show that performing the out-of-sample forecasting of the interest rate curve with the Principal Component Analysis (PCA) method based on the long-term covariance matrix (LRCM) seems to be more accurate compared to PCA based on sample covariance matrix.

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Moraes, Amanda Carolina Prado de [UNESP]. "Diversidade fenotípica e determinação do teor de óleo em pinhão-manso por espectroscopia no infravermelho próximo." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/139509.

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Submitted by AMANDA CAROLINA PRADO DE MORAES null (amandacpmoraes@yahoo.com.br) on 2016-06-22T14:46:47Z No. of bitstreams: 1 Dissertação CORRIGIDA_VERSÃO FINAL (1) (1).pdf: 1286128 bytes, checksum: f9b38615935f79407664f88d4e940988 (MD5) Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-06-22T17:12:25Z (GMT) No. of bitstreams: 1 moraes_acp_me_jabo.pdf: 1286128 bytes, checksum: f9b38615935f79407664f88d4e940988 (MD5) Made available in DSpace on 2016-06-22T17:12:25Z (GMT). No. of bitstreams: 1 moraes_acp_me_jabo.pdf: 1286128 bytes, checksum: f9b38615935f79407664f88d4e940988 (MD5) Previous issue date: 2016-04-29 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) 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.

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Pirotta, Melina Zacarelli [UNESP]. "Resistência ao tripes-do-prateamento e seleção em genótipos interespecíficos de amendoim." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/141884.

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Submitted by MELINA ZACARELLI PIROTTA null (melina_pirotta@hotmail.com) on 2016-07-18T21:21:22Z No. of bitstreams: 1 Dissertação MelinaPirotta.pdf: 1657280 bytes, checksum: 28a5e7bf7037eb99bdc6bf02f5f176a0 (MD5) Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-07-20T13:26:33Z (GMT) No. of bitstreams: 1 pirotta_mz_me_jabo.pdf: 1657280 bytes, checksum: 28a5e7bf7037eb99bdc6bf02f5f176a0 (MD5) Made available in DSpace on 2016-07-20T13:26:33Z (GMT). No. of bitstreams: 1 pirotta_mz_me_jabo.pdf: 1657280 bytes, checksum: 28a5e7bf7037eb99bdc6bf02f5f176a0 (MD5) Previous issue date: 2016-05-25 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) O amendoim (Arachis hypogaea L.) é uma cultura oleaginosa de grande importância para o agronegócio brasileiro. Um dos principais fatores que afetam sua produção é a incidência de pragas, com destaque para o tripes-do-prateamento, Enneothrips flavens, Moulton, 1941 (Thysanoptera: Thripidae). Têm-se sugerido que genes que condicionam a resistência genética a esta praga, podem ser encontrados em outras espécies do gênero Arachis L. Entretanto, a utilização de espécies silvestres no melhoramento, torna-se dificultada por barreiras de esterilidade, sendo a maioria devido às diferenças de constituição do genoma e de ploidia. Para contornar essa incompatibilidade, sugeriu-se a obtenção de anfidiploides, resultantes do cruzamento de espécies diploides, seguido da duplicação de seus cromossomos, para então cruzá-los com o amendoim cultivado. Mediante o exposto, este trabalho teve como objetivos estudar o potencial de resistência ao tripes-do-prateamento em populações segregantes iniciais do cruzamento envolvendo a cultivar comercial IAC 503 e o anfidiploide sintético (A. magna x A. cardenasii)4x, monitorar os caracteres indicadores de proximidade agronômica dos segregantes interespecíficos com genótipos da espécie cultivada, bem como a seleção de genótipos superiores por meio de análises uni e multivariadas. Os experimentos foram conduzidos no esquema de blocos aumentados de Federer com testemunhas intercalares em duas gerações: F3, conduzida no ano agrícola de 2013/14 e F4, conduzida em 2014/15, sob infestação natural do inseto. A resistência ao tripes foi avaliada por meio de sua infestação e pelos sintomas de injúrias causadas pelo inseto. Foram avaliados também, alguns componentes de produção como indicadores da proximidade dos genótipos segregantes ao cultivado. Para estes caracteres, foram utilizadas as estimativas dos componentes genéticos, ganho genético e predição dos valores genéticos, calculados via modelos mistos (REML/BLUP). Para as análises multivariadas foram utilizadas as técnicas de componentes principais, análise de agrupamento hierárquico e não hierárquico com os dados das progênies de geração F4. Os resultados deste trabalho demonstram que as análises univariada e multivariada foram eficientes na seleção das melhores progênies, evidenciando a superioridade agronômica das mesmas, quanto aos componentes de produção e de resistência ao tripes. Porém, apesar de algumas progênies segregantes selecionadas como resistentes apresentarem bons componentes de produção, seu grau de adequação agronômica aos genótipos A. hypogaea L. ainda é pequeno. Assim, a identificação de progênies com resistência ao tripes geneticamente próximas às espécies do gênero Arachis L., constitui interessante fonte para estratégias futuras de introgressão gênica, podendo para isso, ser utilizado o método dos retrocruzamentos para inserção dos genes desejáveis de resistência e consequente recuperação dos genótipos adaptados, obtendo-se ao final do processo seletivo, cultivares comerciais portadoras de resistência ao tripes e com caracteres agronômicos e comerciais superiores. The peanut (Arachis hypogaea L.) is an oilseed crop of great importance for Brazilian agribusiness. One of the main factors affecting its production is pests incidence, mainly thrips, Enneothrips flavens, Moulton, 1941 (Thysanoptera: Thripidae). There have suggested that genetic resistance to this pest can be found in other species of the genus Arachis L. However, the use of wild species in breeding was hampered by sterility barriers, mostly due to differences in the genome constitution and ploidy. To work around this incompatibility, it was suggested obtaining amphidiploid, resulting from the crossing of diploid species, followed by duplication its chromosomes, and then cross them with cultivated peanut. Through the above, this study aimed to study the potential for resistance to thrips in early segregating populations crossing involving commercial cultivar IAC 503 and synthetic amphidiploid (A. magna x A. cardenasii) 4x, monitor the agronomic traits proximity indicators of interspecific segregating with cultivated species, as well as the selection of superior genotypes using univariate and multivariate analyses. The experiments were conducted on the Federer augmented blocks with additional checks in two generations: F3, conducted in the agricultural year of 2013/14 and F4, conducted in 2014/15, under natural insect infestation. The thrips resistance was evaluated by its infestation, and the symptoms of injuries caused by insects. It was also evaluated, some production components such as proximity indicators of segregating the genotypes grown. To do so, estimates were used genetic components, genetic gain and prediction of breeding values calculated by mixed models (REML/BLUP). For multivariate analyses were used progenies in F4 generation, through technique principal components and cluster analysis. The results show that the univariate and multivariate analyses were effective in selection the best progenies, demonstrating the agronomic superiority of the same, as the components of production and resistance to thrips. However, despite some segregating progenies selected as resistance present good production components, the degree of agronomic suitability to the genotype A. hypogaea L. is still small. Thus, the identification of progenies with genetic resistance to thrips next to genus Arachis L. is an interesting source for future strategies of gene introgression, using the backcrossing method for insertion desirable genes of resistance and posteriorly recovery of adapted genotypes, getting to the end of the selective process, commercial cultivars with thrips resistance and good agronomic traits.

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Silva, Nilza Regina da 1950. "Aplicativo computacional para utilização de componentes principais em experimentação agronômica /." Botucatu : [s.n.], 2005. http://hdl.handle.net/11449/90718.

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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

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Ragozzine, Brett A. "Modeling the Point Spread Function Using Principal Component Analysis." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1224684806.

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Rao, Naresh Krishna. "Scheduling optimal maintenance times for a system based on component reliabilities." Diss., Virginia Tech, 1992. http://scholar.lib.vt.edu/theses/available/etd-05042006-164506/.

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Valle-Cervantes, Sergio. "Plant-wide monitoring of processes under closed-loop control." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3035991.

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Davis, Christopher Brent Busch Kenneth W. Busch Marianna A. "Application of chemometric analysis to UV-visible and diffuse near-infrared reflectance spectra." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5042.

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Tesi sul tema "Principal components"

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