Browsing by Author "Jacqueline Hughes-Oliver, Committee Member"

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Electrostatic Generation and Control on Textiles.
(2010-08-06) Liu, Lu; Abdel-fattah Seyam, Committee Chair; William Oxenham, Committee Chair; Jacqueline Krim, Committee Member; Pamela Banks-Lee, Committee Member; Thomas Theyson, Committee Member; Jacqueline Hughes-Oliver, Committee Member
An Exploration of the Spatial Dependence Structure of Crop Yields and the Implications for Crop Insurance
(2003-02-03) DiRienzo, Cassandra Elizabeth; Ray Palmquist, Committee Member; Jacqueline Hughes-Oliver, Committee Member; Paul Fackler, Committee Co-Chair; Barry Goodwin, Committee Co-Chair
Systemic risk, in regard to agricultural production, refers to the spatial dependence of crop yields stemming from correlated weather, soil patterns, and other geographically related factors. Systemic risk has been named as a contributing factor to the Federal Crop Insurance Corporation's (FCIC) poor actuarial performance. To date, little research has explored the spatial dependence structure of crop yields. This paper explores three aspects of the spatial dependence structure of yields; the cross-crop spatial correlation structure, the rate of crop yield spatial correlation decay, and the characteristics of the bivariate distributions which define the spatial relationship of crop yields. This paper is divided into six sections. Section One provides a history of the FCIC and a literature review concerning the poor actuarial performance of the FCIC. Section Two discusses the data used in the analysis sections of this paper. Section Three uses two non-parametric tests to explore cross-crop spatial correlation. Section Four develops a model to describe the rate of crop yield spatial correlation decay. Section Five uses the copula methodology to find the bivariate copula distribution that best describes the spatial relationship of crop yields. Section Six summarizes and concludes the research performed in this paper.
Improving Forensic Identification Using Bayesian Networks and Relatedness Estimation: Allowing for Population Substructure
(2005-11-10) Hepler, Amanda B; Jacqueline Hughes-Oliver, Committee Member; Jung-Ying Tzeng, Committee Member; Maria Oliver-Hoyo, Committee Member; Bruce Weir, Committee Chair
Population substructure refers to any population that does not randomly mate. In most species, this deviation from random mating is due to emergence of subpopulations. Members of these subpopulations mate within their subpopulation, leading to different genetic properties. In light of recent studies on the potential impacts of ignoring these differences, we examine how to account for population substructure in both Bayesian Networks and relatedness estimation. Bayesian Networks are gaining popularity as a graphical tool to communicate complex probabilistic reasoning required in the evaluation of DNA evidence. This study extends the current use of Bayesian Networks by incorporating the potential effects of population substructure on paternity calculations. Features of HUGIN (a software package used to create Bayesian Networks) are demonstrated that have not, as yet, been explored. We explore three paternity examples; a simple case with two alleles, a simple case with multiple alleles, and a missing father case. Population substructure also has an impact on pairwise relatedness estimation. The amount of relatedness between two individuals has been widely studied across many scientific disciplines. There are several cases where accurate estimates of relatedness are of forensic importance. Many estimators have been proposed over the years, however few appropriately account for population substructure. Thus, a new maximum likelihood estimator of pairwise relatedness is presented. In addition, a novel method for relationship classification is derived. Simulation studies compare these estimators to those that do not account for population substructure. The final chapter provides real data examples demonstrating the advantages of these new methodologies.
Quantifying Shared Information Value in a Supply Chain Using Decentralized Markov Decision Processes with Restricted Observations
(2005-09-27) Wei, Wenbin; Jacqueline Hughes-Oliver, Committee Member; Henry Nuttle, Committee Member; Thom Hodgson, Committee Co-Chair; Russell King, Committee Co-Chair
Information sharing in a two-stage and three-stage supply chain is studied. Assuming the customer demand distribution is known along the supply chain, the information to be shared is the inventory level of each supply chain member. In order to study the value of shared information, the supply chain is examined under different information sharing schemes. A Markov decision process (MDP) approach is used to model the supply chain, and the optimal policy given each scheme is determined. By comparing these schemes, the value of shared information can be quantified. Since the optimal policy maximizes the total profit within a supply chain, allocation of the profit among supply chain members, or transfer cost/price negotiation, is also discussed. The information sharing schemes include full information sharing, partial information sharing and no information sharing. In the case of full information sharing, the supply chain problem is modeled as a single agent Markov decision process with complete observations (a traditional MDP) which can be solved based on the policy iteration method of Howard (1960). In the case of partial information sharing or no information sharing, the supply chain problem is modeled as a decentralized Markov decision process with restricted observations (DEC-ROMDP). Each agent may have complete observation of the process, or may have only restricted observation of the process. In order to solve the DEC-ROMDP, an evolutionary coordination algorithm is introduced, which proves to be effective if coupled with policy perturbation and multiple start strategies.