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Indiana University-Purdue University Indianapolis (IUPUI) / Relationships among neighbors contribute to the well-being and outcomes of all who live within a neighborhood. Existing literature provides us with a wealth of information on individual seniors’ isolation but does not seem to consider how neighborhood factors add to the attachment of seniors. Given the increasing number of seniors in our society who have the ability to remain living in their neighborhoods as they age, this study focuses on understanding neighborhood attachment to seniors living in the community. Furthermore, emphasis was placed on the potential impact that senior attachment could have on youth as one subset life stage who reside in a neighborhood. This thesis describes the characteristics of neighborhoods that foster low, normal, and high levels of senior attachment in urban areas and explores the relationship this attachment has to neighborhood youth outcomes. This research opens the door for other scholars to begin to place greater emphasis on the understanding of neighborhood dynamics, intergenerational ties to seniors, and the well being of residents across the life course.

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建立模型來估計匯率早已行之有年。較早期的匯率模型，不論是在樣本內的配適或是樣本外的預測，其實表現的並不理想。之後的研究針對這樣的結果指出，這是因為匯率的表現是非線性的，並非傳統線性模型可描繪出來。而對於捕捉匯率非線性的特性，傾向使用無母數的估計方式。因此，本研究採用最近鄰域法進行美元兌新台幣的匯率預測。另外，許多早期的研究發現，隨機漫步模型與其他模型相比較之後，在匯率預測上的表現最好，因而引發了”打敗隨機漫步”的一連串熱潮。本研究欲延續這項議題，將隨機漫步模型做為與最近鄰域模型比較的基準。 / 本研究使用的資料為即期匯率，包含日資料、週資料和月資料三種。將每種資料皆切割為樣本內與樣本外兩個部分，其中最後三分之一的樣本數用於樣本外預測。平均絕對誤差與平均誤差平方根則是用來衡量比較模型預測的準確性。實證結果發現，使用局部加權估計的最近鄰域模型在樣本內的配適表現上優於隨機漫步模型；然而，在樣本外的預測能力上，隨機漫步模型仍舊略勝一籌。 / A wide variety of empirical exchange rate models have been estimated over the years. Earlier findings indicated that exchange rate equations do not fit particularly well, and forecast no better. Later researches then provided a potential reason for the poor performance that traditional exchange rate models, because they are nonlinear. To find a resolution for nonlinearity, nonparametric techniques tend to be useful tools. In this study, we use one of nonparametric techniques called nearest-neighbors method to predict NTD against USD. Besides, many earlier papers found that forecasts from popular models for the foreign exchange rate generally fail to improve upon the random walk out-of-sample. “Beat the random walk” became an emerging issue then. This has motivated this research, and thus we include the random walk as a linear benchmark. / The data set consists of the daily, weekly and monthly spot rates for NTD/USD. We divide each data set into a fitting set and a prediction set for in-sample analysis and out-of-sample forecast, respectively. The out-of-sample forecasts are calculated from the last one-third of each series. As a measure of performance the mean squared error (MAE) and root mean squared error (RMSE) are used. In our empirical results, we find that nearest-neighbors model using local weights easily tops the random walk in-sample. However, as we turn to the out-of-sample prediction, no models produce forecasts superior to the random walk. It seems difficult to beat the random walk out-of-sample in this study.

In patients with age-related macular degeneration, a monolayer of cells in the eyes called retinal pigment epithelium differ from healthy ones in morphology. It is therefore important to quantify the morphological changes, which will help us better understand the physiology, disease progression and classification. Classification of the RPE morphometry has been accomplished with whole tissue data. In this work, we focused on the spatial aspect of RPE morphometric analysis. We used the second-order spatial analysis to reveal the distinct patterns of cell clustering between normal and diseased eyes for both simulated and experimental human RPE data. We classified the mouse genotype and age by the k-Nearest Neighbors algorithm. Radially aligned regions showed different classification power for several cell shape variables. Our proposed methods provide a useful addition to classification and prognosis of eye disease noninvasively.

Com o vislumbramento de aplicações que exigiam representações em espaços multidimensionais, surgiu a necessidade de desenvolvimento de métodos de acessos eficientes a estes dados representados em R^d. Dentre as aplicações precursoras dos métodos de acessos multidimensionais, podemos citar os sistemas de geoprocessamento, aplicativos 3D e simuladores. Posteriormente, os métodos de acessos multidimensionais também apresentaram-se como uma importante ferramenta no projeto de classificadores, principalmente classificadores pelos vizinhos mais próximos. Com isso, expandiu-se o espaço de representação, que antes se limitava no máximo a quatro dimensões, para dimensionalidades superiores a mil. Dentre os vários métodos de acesso multidimensional existentes, destaca-se uma classe de métodos baseados em árvores balanceadas com representação em R^d. Estes métodos constituem evoluções da árvore de acesso unidimenisonal B-tree e herdam várias características deste último. Neste trabalho, apresentamos alguns métodos de acessos dessa classe de forma a ilustrar a idéia central destes algoritmos e propomos e implementamos um novo método de acesso, a PCA-tree. A PCA-tree utiliza uma heurística de quebra de nós baseada na extração da componente principal das amostras a serem divididas. Um hiperplano que possui essa componente principal como seu vetor normal é definido como o elemento que divide o espaço associado ao nó. A partir dessa idéia básica geramos uma estrutura de dados e algoritmos que utilizam gerenciamento de memória secundária como a B-tree. Finalmente, comparamos o desempenho da PCA-tree com o desempenho de alguns outros métodos de acesso da classe citada, e apresentamos os prós e contras deste novo método de acesso através de análise de resultados práticos. / The advent of applications demanding the representation of objects in multi-dimensional spaces fostered the development of efficient multi-dimensional access methods. Among some early applications that required multi-dimensional access methods, we can cite geo-processing systems, 3D applications and simulators. Later on, multi-dimensional access methods also became important tools in the design of classifiers, mainly of those based on nearest neighbors technique. Consequently, the dimensionality of the spaces has increased, from earlier at most four to dimensionality larger than a thousand. Among several multi-dimensional access methods, the class of approaches based on balanced tree structures with data represented in Rd has received a lot of attention. These methods constitute evolues from the B-tree for unidimensional accesses, and inherit several of its characteristics. In this work, we present some of the access methods based on balanced trees in order to illustrate the central idea of these algorithms, and we propose and implement a new multi-dimensional access method, which we call PCA-tree. It uses an heuristic to break nodes based on the principal component of the sample to be divided. A hyperplane, whose normal is the principal component, is defined as the one that will split the space represented by the node. From this basic idea we define the data structure and the algorithms for the PCA-tree employing secondary memory management, as in B-trees. Finally, we compare the performance of the PCA-tree with the performance of other methods in the cited class, and present advantages and disadvantages of the proposed access method through analysis of experimental results.

Com o vislumbramento de aplicações que exigiam representações em espaços multidimensionais, surgiu a necessidade de desenvolvimento de métodos de acessos eficientes a estes dados representados em R^d. Dentre as aplicações precursoras dos métodos de acessos multidimensionais, podemos citar os sistemas de geoprocessamento, aplicativos 3D e simuladores. Posteriormente, os métodos de acessos multidimensionais também apresentaram-se como uma importante ferramenta no projeto de classificadores, principalmente classificadores pelos vizinhos mais próximos. Com isso, expandiu-se o espaço de representação, que antes se limitava no máximo a quatro dimensões, para dimensionalidades superiores a mil. Dentre os vários métodos de acesso multidimensional existentes, destaca-se uma classe de métodos baseados em árvores balanceadas com representação em R^d. Estes métodos constituem evoluções da árvore de acesso unidimenisonal B-tree e herdam várias características deste último. Neste trabalho, apresentamos alguns métodos de acessos dessa classe de forma a ilustrar a idéia central destes algoritmos e propomos e implementamos um novo método de acesso, a PCA-tree. A PCA-tree utiliza uma heurística de quebra de nós baseada na extração da componente principal das amostras a serem divididas. Um hiperplano que possui essa componente principal como seu vetor normal é definido como o elemento que divide o espaço associado ao nó. A partir dessa idéia básica geramos uma estrutura de dados e algoritmos que utilizam gerenciamento de memória secundária como a B-tree. Finalmente, comparamos o desempenho da PCA-tree com o desempenho de alguns outros métodos de acesso da classe citada, e apresentamos os prós e contras deste novo método de acesso através de análise de resultados práticos. / The advent of applications demanding the representation of objects in multi-dimensional spaces fostered the development of efficient multi-dimensional access methods. Among some early applications that required multi-dimensional access methods, we can cite geo-processing systems, 3D applications and simulators. Later on, multi-dimensional access methods also became important tools in the design of classifiers, mainly of those based on nearest neighbors technique. Consequently, the dimensionality of the spaces has increased, from earlier at most four to dimensionality larger than a thousand. Among several multi-dimensional access methods, the class of approaches based on balanced tree structures with data represented in Rd has received a lot of attention. These methods constitute evolues from the B-tree for unidimensional accesses, and inherit several of its characteristics. In this work, we present some of the access methods based on balanced trees in order to illustrate the central idea of these algorithms, and we propose and implement a new multi-dimensional access method, which we call PCA-tree. It uses an heuristic to break nodes based on the principal component of the sample to be divided. A hyperplane, whose normal is the principal component, is defined as the one that will split the space represented by the node. From this basic idea we define the data structure and the algorithms for the PCA-tree employing secondary memory management, as in B-trees. Finally, we compare the performance of the PCA-tree with the performance of other methods in the cited class, and present advantages and disadvantages of the proposed access method through analysis of experimental results.