In this reviewwe discuss methodology to ascertain the amount of information in given data sets with respect to determination of model parameters with desired levels of uncertainty.We do this in the context of least squares (ordinary,weighted, iterative reweightedweighted or "generalized", etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in the biological and environmental sciences.

The Burr families (Type III and Type XII) of distributions are traditionally used in the context of statistical modeling and for simulating non-normal distributions with moment-based parameters (e.g., Skew and Kurtosis). In educational and psychological studies, the Burr families of distributions can be used to simulate extremely asymmetrical and heavy-tailed non-normal distributions. Conventional moment-based estimators (i.e., the mean, variance, skew, and kurtosis) are traditionally used to characterize the distribution of a random variable or in the context of fitting data. However, conventional moment-based estimators can (a) be substantially biased, (b) have high variance, or (c) be influenced by outliers. In view of these concerns, a characterization of the Burr Type III and Type XII distributions through the method of L-moments is introduced. Specifically, systems of equations are derived for determining the shape parameters associated with user specified L-moment ratios (e.g., L-Skew and L-Kurtosis). A procedure is also developed for the purpose of generating non-normal Burr Type III and Type XII distributions with arbitrary L-correlation matrices. Numerical examples are provided to demonstrate that L-moment based Burr distributions are superior to their conventional moment based counterparts in the context of estimation, distribution fitting, and robustness to outliers. Monte Carlo simulation results are provided to demonstrate that L-moment-based estimators are nearly unbiased, have relatively small variance, and are robust in the presence of outliers for any sample size. Simulation results are also provided to show that the methodology used for generating correlated non-normal Burr Type III and Type XII distributions is valid and efficient. Specifically, Monte Carlo simulation results are provided to show that the empirical values of L-correlations among simulated Burr Type III (and Type XII) distributions are in close agreement with the specified L-correlation matrices.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18128号 / 医博第3848号 / 新制||医||1001(附属図書館) / 30986 / 京都大学大学院医学研究科医学専攻 / (主査)教授 福山 秀直, 教授 戸井 雅和, 教授 平岡 眞寛 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19172号 / 医博第4014号 / 新制||医||1010(附属図書館) / 32164 / 京都大学大学院医学研究科医学専攻 / (主査)教授 武藤 学, 教授 平岡 眞寛, 教授 川口 義弥 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20232号 / 医博第4191号 / 新制||医||1019(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 戸井 雅和, 教授 武田 俊一, 教授 鈴木 実 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・論文博士 / 博士(医学) / 乙第13218号 / 論医博第2165号 / 新制||医||1033(附属図書館) / 京都大学大学院医学研究科内科系専攻 / (主査)教授 溝脇 尚志, 教授 黒田 知宏, 教授 鈴木 実 / 学位規則第4条第2項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Distribution load flow and state estimation are two important functions in distribution energy management systems (DEMS) and advanced distribution automation (ADA) systems. Distribution load flow analysis is a tool which helps to analyze the status of a power distribution system under steady-state operating conditions. In this research, an effective and comprehensive load flow algorithm is developed to extensively incorporate the distribution system components. Distribution system state estimation is a mathematical procedure which aims to estimate the operating states of a power distribution system by utilizing the information collected from available measurement devices in real-time. An efficient and computationally effective state estimation algorithm adapting the weighted-least-squares (WLS) method has been developed in this research. Both the developed algorithms are tested on different I testeeders and the results obtained are justified.

Open space provides a wide range of ecosystem services to communities. In growing communities, open space offers relief from congestion and other negative externalities associated with rapid development. To make effective policy and planning decisions pertaining to open space preservation, it is important to estimate monetary values of its benefits. In addition, assessing public opinions regarding open space provides information on demand and how residents value open space. This study estimated the monetary value of open space in Mississippi and Alabama Gulf Coast communities. The study also collected information on coastal residents’ attitudes towards open space, working waterfronts, and their willingness to support waterfront open space preservation monetarily. Two methodological approaches were employed to estimate the monetary value of waterfront open space: contingent valuation (CVM) and hedonic price (HPM) methods. Data were collected using a mail survey, the Multiple Listing Service (MLS), and publicly available data sources such as the U.S. Census. Data were analyzed using an interval regression, ordinary least squares, and geographically weighted regression (GWR) models. Mail survey results indicated that the majority of residents valued open space and were willing to pay from $80.52 to $162.14 per household as estimated by four different interval-censored econometric models. Respondent’s membership in groups promoting conservation goals, income, age, and residence duration were major factors associated with their willingness to pay. Results from the HPM indicated proximities to waterfronts, with the exception of bayous, were positively related to home prices, suggesting open space produced positive economic benefits. Findings from the HPM analysis using publicly available data were consistent and comparable with the results from the HPM that used MLS data. This similarity of results indicates the use of publicly available data is feasible in HPM analysis, which is important for broad applications of the method during city planning. In addition, GWR estimates provided site specific monetary values of waterfront open space benefits, which will be helpful for policymakers and city planners in developing site-specific conservation and preservation strategies. Findings can help formulate future decisions related to alternative development scenarios of coastal areas and conservation efforts to preserve open space.