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Robuste Asset-Allocation /Brinkmann, Ulf. January 2007 (has links)
Zugl.: Bremen, Universiẗat, Diss., 2007.
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Schätzrisiken in der Portfoliotheorie : Auswirkungen und Möglichkeiten der Reduktion /Memmel, Christoph. January 2004 (has links)
Zugl.: Köln, Universiẗat, Diss., 2004.
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Selection of a multiple disease resistant runner-type peanutBaring, Michael Robert 17 September 2007 (has links)
Four F2:4 populations of peanut (Arachis hypogaea L.) resulting from the complex
cross Tamrun 96 X Tx901639-3 X Sun Oleic 95R were grown in three disease nurseries
over a 2 year period. Three separate selection techniques were applied to determine
which technique would provide the most effective method for selecting a multiple
disease resistant, runner-type peanut. Technique I involved selection at a tomato spotted
wilt virus nursery during the first cycle of selection and transferring the selections to a
Sclerotinia minor (Jagger) nursery for a second cycle of selection in year two.
Technique II was the reciprocal of Technique I. Technique III involved selection of the
populations at a multiple disease nursery for two consecutive years. Selections were
based on disease ratings, growth habits, pod and seed characteristics, and oleic/linoleic
acid ratios. Disease ratings were scored as percentage infection on a scale of 0 (0% plot
infected) to 10 (100% plot infected). Disease severity was also rated on a scale of 1
(symptoms noted, but no yield effects) to 10 (plant death, no yield). There were two
final selections for each population using each selection technique that were yield tested
over a 2 year period to determine which technique was superior. The yield tests were
conducted using completely randomized block design at all three disease nurseries with an additional disease-free site included. Data for disease ratings, yield, grade, and value
per hectare were combined within locations across years. All three selection techniques
provided lines with more disease resistance than the parents; however, there was no
difference detected between the effectiveness of the three techniques in terms of disease
resistance, yield, grade, or value per hectare.
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Improving accuracy of genomic prediction in dairy and beef cattleChen, Liuhong 01 May 2013 (has links)
The overall goal of this thesis was to improve the accuracy of genomic prediction in dairy and beef cattle by developing, evaluating and enhancing novel or existent models and approaches for genomic selection. Four studies were conducted to fulfill this goal. In the first study, the impact of using genotypes imputed from low density panels for genomic prediction was evaluated and compared between a Bayesian mixture model and the Genomic Best Linear Unbiased Prediction (GBLUP) method. Results showed that for traits affected by a few large QTL, the Bayesian mixture model resulted in greater reduction in accuracy of genomic prediction, compared to GBLUP. However, for all SNP panels, scenarios and all traits studied, the Bayesian mixture model produced greater or similar accuracy, compared to the GBLUP method. In the second study, a new computing algorithm, called right-hand side updating strategy (RHSU), was proposed and compared to the conventional Gauss-Seidel residual update algorithm (GSRU) for genomic prediction. Results showed that RHSU would outperform GSRU once the sample size exceeded a fraction of the number of the SNPs. As the sample size continued to grow, the RHSU algorithm became more efficient than GSRU. In the third study, three different strategies of forming a training population for genomic prediction, within-breed, across-breed and pooling data from different breeds, were evaluated in Angus and Charolais steers using phenotypes on residual feed intake (RFI) and genotypes on the Illumina BovineSNP50 Beadchip (50k). Results suggested that using the 50k SNP panel, within-breed genomic prediction was a safe strategy; across-breed prediction resulted in the lowest accuracy; pooling data from different breeds had a potential to improve the accuracy but should be conducted with caution due to possible loss of accuracy. In the last study, a multi-task Bayesian learning model was proposed for multi-population genomic prediction. The performance of the multi-task model was evaluated in Holstein and Ayrshire dairy breeds. Results showed that the multi-task Bayesian learning model is effective and could be beneficial to smaller populations where only a limited number of training animals are available.
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Essays on asset allocation and derivativesSchneider, Eva. Unknown Date (has links) (PDF)
Frankfurt (Main), University, Diss., 2008. / Erscheinungsjahr an der Haupttitelstelle: 2007.
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The selection of jurors a comparative study of the methods of selection and the personnel of juries in Philadelphia and other cities /Callender, Clarence N. January 1924 (has links)
Thesis (Ph.D.)--University of Pennsylvania, 1924. / Reproduction of original from Yale Law School Library. Bibliography: p. 104-107.
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Portfolio-Optimierung nach Markowitz /Mertens, Detlef. January 2004 (has links)
Thesis (doctoral)--Wiss. Hochsch. für Unternehmensführung, Vallendar, 2004.
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Messung und Analyse der Performance von Aktienportfolios : theoretische Grundlagen ausgewählter Konzepte und deren praktische Bedeutung /Stahlhut, Bettina. January 1997 (has links)
Zugl.: Frankfurt (Main), Hochsch. für Bankwirtschaft, Diplomarbeit, 1997.
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Aktienprognosen zur Portfolio-Optimierung /Marx, Stefan. January 1996 (has links)
Zugl.: Berlin, Techn. Universiẗat, Diss., 1996.
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Modelling, estimating and validating multidimensional distribution functions with applications to risk management /Junker, Markus. January 1900 (has links) (PDF)
Kaiserslautern, Techn. University, Diss., 2003.
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