Aggregating financial assets together to form a portfolio, commonly referred to as "asset pooling", is a standard practice in the banking and insurance industries. Determining a suitable probability distribution for this portfolio with each underlying asset is a challenging task unless several distributional assumptions are made. On the other hand, imposing assumptions on the distribution inhibits its ability to capture various idiosyncratic behaviors. It limits the model's usefulness in its ability to provide realistic risk metrics of the true portfolio distribution. In order to conquer this limitation, we propose two methods to model a pool of assets with much less assumptions on the correlation structure by way of finding analytical bounds. Our first method uses the Fre??chet-Hoeffding copula bounds to calculate model-free upper and lower bounds for aggregate assets evaluation. For the copulas with specific constraints, we improve the Fre??chet- Hoeffding copula bounds by providing bounds with narrower range. The improvements proposed are very robust for different types of constraints on the copula function. However, the lower copula bound does not exist for dimension three and above. Our second method tackles the open problem of finding lower bounds for higher dimensions by introducing the concept of Complete Mixability property. With such technique, we are able to find the lower bounds with specified constraints. Three theorems are proposed. The first theorem deals with the case where all marginal distributions are identical. The lower bound defined by the first theorem is sharp under some technical assumptions. The second theorem gives the lower bound in a more general setup without any restriction on the marginal distributions. However the bound achieved in this context is not sharp. The third theorem gives the sharp lower bound on Conditional VaR. Numerical results are provided for each method to demonstrate sharpness of the bounds. Finally, we point out some possible future research directions, such as looking for a general sharp lower bound for high dimensional correlation structures.

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Recent scientific support for the involvement of genetic locus interaction in quantitative trait variation and the widespread use of quantitative trait locus (QTL) mapping has resulted in the need to examine those aspects concurrently. Computer software was written to simulate interacting quantitative trait loci (QTLs) in plant populations. Using this software, interacting QTLs were simulated to examine effects of epistasis on the detection of QTLs and the quality of QTL parameter estimates. Simulations involved doubled haploid populations exhibiting two non-epistatic traits and seven epistatic traits, each trait at four levels of heritability. Detection efficiency of QTL main and interaction effects decreased with decreasing heritability. At a given level of broad-sense heritability, traits differed with respect to the relative quality of main-effect detection and interaction-effect detection. Main-effect detection was notably poor for one epistatic locus that has a relatively small additive effect. Position estimates were accurate but their precision deteriorated with decreasing heritability. The quality of QTL effect estimates declined consistently with decreasing heritability, and loss in the accuracy was associated with losses in power of detection.

Identifying the genetic basis for phenotypic variation is a central question in evolutionary biology and can be studied in detail using model organisms. Fecundity variation in different isolates of C. briggsae has been observed previously, but the genetic causes of this variation are unclear. Crosses between C. briggsae advanced-intercross recombinant inbred lines (AI-RILs) and parental strains yield near isogenic line (NIL) strains, which I created to provide a powerful genetic resource to fine-map the basis for fecundity and other trait differences. Phenotypic analysis of the NILs shows the complexities of possible epistatic interactions on phenotypic expression. These NIL strains contribute a valuable genetic resource toward the long-term goal of identifying the genes responsible for differences in fecundity in this species. The elucidation of the basis for this trait variation will also contribute further into the mechanisms for how genotype and phenotype and environment all interact.

Identifying the genetic basis for phenotypic variation is a central question in evolutionary biology and can be studied in detail using model organisms. Fecundity variation in different isolates of C. briggsae has been observed previously, but the genetic causes of this variation are unclear. Crosses between C. briggsae advanced-intercross recombinant inbred lines (AI-RILs) and parental strains yield near isogenic line (NIL) strains, which I created to provide a powerful genetic resource to fine-map the basis for fecundity and other trait differences. Phenotypic analysis of the NILs shows the complexities of possible epistatic interactions on phenotypic expression. These NIL strains contribute a valuable genetic resource toward the long-term goal of identifying the genes responsible for differences in fecundity in this species. The elucidation of the basis for this trait variation will also contribute further into the mechanisms for how genotype and phenotype and environment all interact.

No description available.

[Abstract]: Crown rot (causal organism: Fusarium pseudograminearum) is a significantdisease affecting wheat in Australia. Although first reported over 60 years ago, the disease has become more prevalent in recent years due to the adoption ofminimum tillage and stubble retention practices. Breeding for resistance to crown rot is difficult - phenotypic selection, which is usually done at harvest, istime-consuming, expensive, and subject to between year variability due to sensitivity to environmental conditions. For these reasons, the coupling ofmolecular techniques with conventional plant breeding (marker-assisted selection) has the potential to more rapidly and reliably identify genomic regionsthat contribute to resistance. The objective of this study was to identify, validate,and pyramid quantitative trait loci (QTL) for resistance to crown rot present in aW21MMT70 x Mendos doubled haploid wheat population.Replicated seedling trials were conducted in 2001, 2003, and 2005. In eachseedling trial, W21MMT70 displayed partial resistance to crown rot whereasMendos seedlings were susceptible. A bulked segregant analysis (BSA), using390 simple sequence repeat (SSR) markers chosen for their coverage of thewheat genome, was initially conducted based upon the 2001 seedling trial data inan attempt to rapidly identify genomic regions associated to resistance. The BSAdid not reveal any markers associated with resistance to crown rot. As a result, afull mapping study was conducted. One hundred and twenty eight (128) SSRmarkers were mapped across the population to produce a framework map.Previously screened AFLP markers were added to the map. Composite intervalmapping revealed eight QTL associated with resistance. Of these, three (locatedon chromosomes 2B, 2D, and 5D) were consistently detected in each of the threeseedling trials. Two QTL (on chromosomes 1A and 3B) were detected in two ofthe three trials. The 2D, 3B, and 5D QTL were inherited from W21MMT70,whereas the 1A and 2B QTL were inherited from Mendos.Two software programs were used to identify epistatic interactions betweenQTL. While the results of the two programs differed markedly, both programsdetected a highly significant interaction between the W21MMT70 inherited 5DQTL and a locus on chromosome 2D inherited from Mendos. The overall effectof the epistatic interactions was not as great as the additive effects of nonepistaticQTL. Nonetheless, the presence of epistasis may indicate that,particularly in the case of 5D, the effect of this QTL may be dependent on thebackground into which it is introgressed.Validation of three W21MMT70-inherited QTL (on chromosomes 2D, 3B, and5D) was conducted on three F2 populations with W21MMT70 as one of theparents. While the 5D QTL was validated in two of the three crosses, neither the2D nor the 3B QTL were detected in any of the F2 validation populations. It islikely that the size of the F2 populations (the largest composed of 94 individuals),in conjunction with the variability that is inherent when screening for resistanceto crown rot, precluded validation of these regions. Validation of the 2BMendos-inherited QTL was conducted on a Sunco x Batavia doubled haploidpopulation because Sunco possesses the same Triticum timopheevi 2B introgression that is present in Mendos. This validated QTL (designatedQ.CR..usq-2B2) explained 11 % of the phenotypic variance in the Sunco xBatavia population.To assess the effectiveness of pyramiding QTL for resistance to crown rot, a 2-49x W21MMT70 population was examined. A number of lines of this populationperformed significantly better than each of the parents in the replicated seedlingtrial that was conducted. Four QTL, located on chromosomes 1A, 1D, 2D, and3B, were detected. The 1A and 1D QTL were inherited from 2-49 whereas the2D and 3B QTL were inherited from W21MMT70. The 1A QTL from 2-49 hasnot been previously validated, and this QTL has been designated QCr.usq-1A1.The 3B QTL (designated QCr.usq-3B1) had the highest effect (LRS 42.1;explaining 21.0 % of the phenotypic variance) in the 2-49 x W21MMT70population. The 2D QTL (QCr.usq-2D1) was shown to have a minor effect. The5D QTL that was inherited from W21MMT70 in the W21MMT70 x Mendospopulation was not detected in the 2-49 x W21MMT70 population. A number ofpossible explanations for the inability to detect this QTL in the 2-49 xW21MMT70 population are discussed.

Universiẗat, Diss., 2006--Frankfurt (Main). / Enth. Sonderabdr. aus Zeitschr. - Beitr. teilw. dt., teilw. eng. - Zsfassung in dt. und engl. Sprache.

Würzburg, Universiẗat, Diss., 2007.