Master of Science / Department of Industrial & Manufacturing Systems Engineering / Chih-Hang Wu / The fast advancement in computer technologies in the recent years has made the use of simulation to estimate stock/equity performances and pricing possible; however, determining the optimal exercise time and prices of American options using Monte-Carlo simulation is still a computationally challenging task due to the involved computer memory and computational complexity requirements. At each time step, the investor must decide whether to exercise the option to get the immediate payoff, or hold on to the option until a later time. Traditionally, the stock options are simulated using Monte-Carlo methods and all stock prices along the path are stored, and then the optimal exercise time is determined starting at the final time period and continuing backward in time. Also, as the number of paths simulated increases, the number of simultaneous equations that need to be solved at each time step grow proportionally. Currently, two theoretical methods have emerged in determining the optimal exercise problem. The first method uses the concept of least-squares approach in linear regression to estimate the value of continuing to hold on to the option via a set of randomly generated future stock prices. Then, the value of continuing can be compared to the payoff at current time from exercising the option and a decision can be reached, which gives the investor a higher value. The second method uses the finite difference approach to establish an exercise boundary for the American option via an artificially generated mesh on both possible stock prices and decision times. Then, the stock price is simulated and the method checks to see if it is inside the exercise boundary. In this research, these two solution approaches are evaluated and compared using discrete event simulation. This allows complex methods to be simulated with minimal coding efforts. Finally, the results from each method are compared. Although a more conservative method cannot be determined, the least-squares method is faster, more concise, easier to implement, and requires less memory than the mesh method. The motivation for this research stems from interest in simulating and evaluating complicated solution methods to the optimal exercise problem, yet requiring little programming effort to produce accurate and efficient estimation results.

This Thesis deals with the methods for detection of the outliers and influential points based on method of least squares. The first part of the thesis summarizes the teoretical findings of the method of least squares and both methods for detection of the outliers and influential points based on the method of least squares and also based on robust regression. The practical part of this thesis deals with the application of classic methods for detection of the outliers and influential points on three types of datasets (artifical data, data from specialized literature and real data). The results of the application are subject to qualitative comparisson with the results produced by the methods for detection of the outliers and influentials point based on the robust regression.

[pt] Esse trabalho busca verificar se há relação entre a política monetária conduzida por um grupo de países e as suas instituições, especialmente aquelas ligadas ao setor público. A partir da estimação de uma regra de politica monetária comum para um grupo de países, regredimos coeficientes de reação das autoridades monetárias a desvios da inflação da meta e do hiato da atividade em métricas de instituições. Encontramos relações significativas entre a condução de política monetária e as instituições dos países, bem como potenciais determinantes das instituições, em vários casos. / [en] This paper seeks to verify if there is a relationship between the monetary policy conducted by a group of countries and their institutions, especially those related to the public sector. From the estimation of a common monetary policy rule for a group of countries, we regressed the reaction coefficients of the monetary authorities to deviations from inflation target and activity gap on institutional metrics. We find significant relationships between conducting monetary policy and country institutions, as well as potential determinants of institutions, in several cases.

Orientador: Luiz Felipe Mendes de Moura / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-28T11:40:36Z (GMT). No. of bitstreams: 1 Ferreira_SabrinadosSantos_M.pdf: 4964592 bytes, checksum: 48a2af83f98b244951f9db97d31a68cc (MD5) Previous issue date: 2015 / Resumo: O objetivo deste trabalho foi o estudo da distribuição de temperatura em um domínio retangular, para tal foi resolvida a Equação de Convecção - Difusão Bidimensional via Método dos Elementos Finitos de Mínimos Quadrados - Least Squares Finite Element Method (LSFEM). Para discretização espacial foi utilizado elementos bidimensionais quadráticos, nesse caso os elementos quadriláteros com oito nós foram escolhidos. A discretização temporal nos casos transientes foi aproximada via Método de Crank - Nicolson. Para a o obtenção da matriz do elemento e o vetor do lado direito a quadratura de Gauss - Legendre foi empregada. A solução do sistema algébrico resultante foi obtida a partir do Método dos Gradientes Conjugados, um dos métodos iterativos mais eficientes na resolução de sistemas lineares quando a matriz é simétrica, esparsa e definida positiva, sendo essas características resultantes da formulação via LSFEM. É apresentada a formulação matemática do problema, a metodologia empregada na solução. Para a obtenção dos resultados um código em linguagem C foi implementado, por fim são apresentados os resultados obtidos, as conclusões e sugestões para trabalhos futuros / Abstract: The objective of this work was the study of temperature distribution in a rectangular domain, for that was solved Equation Convection - Diffusion Two-Dimensional via Least Squares Finite Element Method - LSFEM. For spatial discretization was used two-dimensional quadratic elements, in this case the quadrilateral elements with eight nodes were chosen. The time discretization in transient cases was approached via Crank - Nicolson Method. To obtain the element matrix and vector right side of the Gauss - Legendre quadrature was employed. The solution of resulting algebraic system was obtained using Conjugate Gradient Method, one of the most efficient iterative methods for solving linear sistenas when the matrix is ??symmetric, and positive definite sparse, and these resulting characteristics of the formulation via LSFEM. The mathematical formulation of the problem, the methodology used in the solution is shown. To obtain the results a code in C language was implemented, finally is presented results, conclusions and suggestions for future work / Mestrado / Termica e Fluidos / Mestra em Engenharia Mecânica

The purpose of this study is to estimate the relationship between income inequality and immigration in Sweden. To do so, data from the data base Kolada with observations from all 290 municipalities in Sweden is used. As a proxy for income distribution the Gini coefficient is used and as a proxy for immigration the share of foreign born of working age is used. The model also controls for income tax, education level and unemployment level. The dependent variable the Gini coefficient is bounded by a unit interval and it is therefore not possible to simply run a linear regression. Such a model could potentially predict outside the interval. To properly estimate the relationship two approaches are made. Firstly a model is estimated with Ordinary Least Squares (OLS) after the dependent variable is transformed on to the real line through log-odds. Then a model is estimated using beta regression. The study concludes that there is a statistically significant positive correlation between income inequality and immigration in Sweden. The OLS estimated model shows that a 1 unit increase in immigration, on average increases the log-odds of 0.28336 units, ceteris paribus. Beta regression provides perhaps more intuitive results. If immigration increases with 1% the income inequality increases with on average 0.1046%, ceteris paribus. Because of the easier interpretation, among other things, beta regression is determined to be a better estimation method in this study.

In order to improve the performance of LMS (least mean square) algorithm by decreasing the amount of calculations this research proposes to make an update on each step only for those elements from the input data set, that fall within a small window W near the separating hyperplane surface. This work aims to describe in detail the results that can be achieved by using the proposed LMS with window learning algorithm in information systems that employ the methodology of neural network for the purposes of classification.

Orientador: Renata Cardoso Magagnin / Resumo: A praça tem um significado importante na história das cidades brasileiras, pois reflete a cultura e os costumes da sociedade ao longo dos anos. Como elemento de convívio e de integração do espaço urbano, deve promover o encontro, a diversidade e a circulação segura e eficiente. Formada por elementos morfológicos e aspectos positivos e negativos, os diferentes tipos de configuração espacial e composição podem influenciar na qualidade espacial e permanência dos usuários nesse local. Entender como esses elementos são percebidos tanto por pesquisadores quanto pelos usuários pode ser o primeiro passo para identificar quais elementos físicos e morfológicos podem contribuir, ou não, para a escolha dos usuários em permanecer e usufruir desse local. Neste contexto, o objetivo desta pesquisa é propor um instrumento para avaliar a qualidade espacial de praças, a partir dos elementos físicos e morfológicos que compõe quatro planos bidimensionais da praça e de seu entorno imediato (praça, calçada, rua e fachada) e que envolvem os usuários no espaço público. A metodologia consiste na aplicação de multimétodos, sendo o instrumento composto por quatro fases de avaliação: (i) análise dos aspectos físicos dos planos bidimensionais que envolvem os usuários (praça, calçada, rua e fachada), por meio de indicadores de desempenho e um índice denominado Índice de Qualidade Espacial de Praças (IQEP); (ii) análise da influência da forma, dos aspectos e elementos morfológicos da praça e de seu entorno ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The square has an important significance in the history of Brazilian cities because it reflects the culture and customs of the society over the years. As an element of relief and integration of the urban space, it must promote meeting, diversity, safe and efficient circulation. Formed by morphological elements and positive and negative aspects, the different types of spatial configuration and composition can influence the spatial quality and permanence of users in that location. Understanding how these elements are perceived by both researchers and users can be the first step in identifying which physical and morphological elements may or may not contribute to the users' choice to stay and enjoy this place. In this context, the objective of this research is to propose an instrument to assess the spatial quality of the squares, from the physical and morphological elements that make up four two-dimensional planes of the square and its immediate surroundings (square, sidewalk, street and facade) and that involve users in the public space. The methodology consists of the application of multimethod, being the instrument composed of four evaluation phases : (i) analysis of the physical aspects of two-dimensional plans that involve users (square, sidewalk, street and façade), through performance indicators and an index called the Spatial Quality Square Index (in Portuguese Índice de Qualidade Espacial de Praças - IQEP); (ii) analysis of the influence of shape, aspects and morphologi... (Complete abstract click electronic access below) / Mestre

A produção agrícola tem crescido nos últimos anos impulsionada pelo aumento populacional e por avanços tecnológicos. Esse crescimento pode ocasionar impactos ambientais importantes, inclusive a degradação do solo, se não forem realizados o correto planejamento agrícola e manejo do solo, a fim de assegurar uma produção competitiva e sustentável. Para isto, a descrição da variabilidade espacial do solo é necessária, sendo realizada convencionalmente através de coleta e análise de amostras. Entretanto, estes métodos convencionais de levantamento da variabilidade do solo possuem custos elevados e demandam bastante tempo e mãode- obra para serem realizados. Com o aumento da quantidade de informação necessária os custos para descrição da variabilidade espacial do solo podem tornarse um obstáculo, se somente metodologias convencionais são aplicadas. Portanto, métodos alternativos tornam-se necessários para auxiliar no levantamento de atributos do solo em escala adequada ao manejo agrícola. Para isto, são propostas metodologias de espectroscopia de reflectância no Vis-NIR-SWIR, as quais empregam o comportamento espectral do solo de 400 nm a 2500 nm para realizar a quantificação de seus atributos. Isto é possível, pois a informação espectral possui relação direta com os constituintes do solo. Assim, no presente estudo é avaliado o uso de imagens aéreas hiperespectrais na quantificação de atributos do solo, através do método PLSR, e mapeamento destes atributos, empregando krigagem. O desempenho das predições feitas com dados do sensor aéreo é comparado ao obtido com espectros coletados em laboratório. Foi também avaliado, através de experimentos de campo, com diferentes doses de calcário em duas áreas diferentes (textura arenosa e média), o uso de informações espectrais coletadas no campo, em movimento, e em laboratório, com amostras úmidas, para a quantificação de atributos e da necessidade de calagem, pelo método PLSR. Foram obtidos resultados satisfatórios através dos dados do sensor aéreo, principalmente, para a quantificação dos teores de argila, areia e CTC (R2 de 0,73, 0,73 e 0,80, respectivamente). Com relação aos dados obtidos por sensoriamento próximo no campo, os melhores resultados foram obtidos para a área de textura média, com R2 de 0,33, 0,38 e 0,61 para a predição da CTC, V% e da necessidade de calagem, respectivamente. / Agricultural production has increased in the last years stimulated by the population growth and technological advances. This can cause significant environmental impacts including soil degradation if suitable agricultural planning and soil management are not applied in order to ensure a competitive and sustainable production. For this purpose, the soil variability assessment is needed and it is conventionally performed through soil sampling and analysis. However, conventional methods have high costs and require considerable time and labor. When the amount of information needed increases, costs to describe soil spatial variability may become an obstacle if only conventional methodologies are applied. Therefore, alternative methods can help to depict soil properties variability on a scale suitable to soil management. So, Vis-NIR-SWIR reflectance spectroscopy (from 400 nm to 2500 nm) is proposed as a mean to predict soil properties. This is possible because spectral information has a direct relationship with soil constituents and characteristics. Therefore, in this study hyperspectral airborne imagery is evaluated as an information source to be used in soil properties quantification, via the PLSR method, and mapping, using kriging. The performance of the models derived from airborne imagery data was compared with the results obtained by models calculated from laboratory sensor data. The use of spectral information collected in the field (on-thego) was evaluated too using a field experiment in witch different rates of lime were applied. The experiment was allocated in two fields with different soil textures (one with about 100 g kg-1 of clay and other with about 320 g kg-1 of clay). The soil properties prediction based on the on-the-go spectral measurements were compared to predictions made using spectra collect in the laboratory and the PLSR method was used to calculate models. Satisfactory results were obtained with airborne sensor data, especially for clay, sand and CTC quantification (R2 of 0.73, 0.73 and 0.80, respectively). Regarding the on-the-go proximal sensing, better predictions were obtained for the clayey area, with R2 of 0.33, 0.38 and 0.61 for predictions of CEC, base saturation of the soil CEC (V%) and lime requirement, respectively.

Dans cette thèse, nous nous intéressons à une méthode de détection des ruptures dans une suite d’observations appartenant à un ensemble muni d’un noyau semi-défini positif. Cette procédure est une version « à noyaux » d’une méthode des moindres carrés pénalisés. Notre principale contribution est de montrer que, pour tout noyau satisfaisant des hypothèses raisonnables, cette méthode fournit une segmentation proche de la véritable segmentation avec grande probabilité. Ce résultat est obtenu pour un noyau borné et une pénalité linéaire, ainsi qu’une autre pénalité venant de la sélection de modèles. Les preuves reposent sur un résultat de concentration pour des variables aléatoires bornées à valeurs dans un espace de Hilbert, et nous obtenons une version moins précise de ce résultat lorsque l’on supposeseulement que la variance des observations est finie. Dans un cadre asymptotique, nous retrouvons les taux minimax usuels en détection de ruptures lorsqu’aucune hypothèse n’est faite sur la taille des segments. Ces résultats théoriques sont confirmés par des simulations. Nous étudions également de manière détaillée les liens entre différentes notions de distances entre segmentations. En particulier, nous prouvons que toutes ces notions coïncident pour des segmentations suffisamment proches. D’un point de vue pratique, nous montrons que l’heuristique du « saut de dimension » pour choisir la constante de pénalisation est un choix raisonnable lorsque celle-ci est linéaire. Nous montrons également qu’une quantité clé dépendant du noyau et qui apparaît dans nos résultats théoriques influe sur les performances de cette méthode pour la détection d’une unique rupture. Dans un cadre paramétrique, et lorsque le noyau utilisé est invariant partranslation, il est possible de calculer cette quantité explicitement. Grâce à ces calculs, nouveaux pour plusieurs d’entre eux, nous sommes capable d’étudier précisément le comportement de la constante de pénalité maximale. Pour finir, nous traitons de l’heuristique de la médiane, un moyen courant de choisir la largeur de bande des noyaux à base de fonctions radiales. Dans un cadre asymptotique, nous montrons que l’heuristique de la médiane se comporte à la limite comme la médiane d’une distribution que nous décrivons complètement dans le cadre du test à deux échantillons à noyaux et de la détection de ruptures. Plus précisément, nous montrons que l’heuristique de la médiane est approximativement normale centrée en cette valeur. / In this thesis, we focus on a method for detecting abrupt changes in a sequence of independent observations belonging to an arbitrary set on which a positive semidefinite kernel is defined. That method, kernel changepoint detection, is a kernelized version of a penalized least-squares procedure. Our main contribution is to show that, for any kernel satisfying some reasonably mild hypotheses, this procedure outputs a segmentation close to the true segmentation with high probability. This result is obtained under a bounded assumption on the kernel for a linear penalty and for another penalty function, coming from model selection.The proofs rely on a concentration result for bounded random variables in Hilbert spaces and we prove a less powerful result under relaxed hypotheses—a finite variance assumption. In the asymptotic setting, we show that we recover the minimax rate for the change-point locations without additional hypothesis on the segment sizes. We provide empirical evidence supporting these claims. Another contribution of this thesis is the detailed presentation of the different notions of distances between segmentations. Additionally, we prove a result showing these different notions coincide for sufficiently close segmentations.From a practical point of view, we demonstrate how the so-called dimension jump heuristic can be a reasonable choice of penalty constant when using kernel changepoint detection with a linear penalty. We also show how a key quantity depending on the kernelthat appears in our theoretical results influences the performance of kernel change-point detection in the case of a single change-point. When the kernel is translationinvariant and parametric assumptions are made, it is possible to compute this quantity in closed-form. Thanks to these computations, some of them novel, we are able to study precisely the behavior of the maximal penalty constant. Finally, we study the median heuristic, a popular tool to set the bandwidth of radial basis function kernels. Fora large sample size, we show that it behaves approximately as the median of a distribution that we describe completely in the setting of kernel two-sample test and kernel change-point detection. More precisely, we show that the median heuristic is asymptotically normal around this value.

The thesis deals with methods of identification of dynamic systems in time and frequency domain. Nonparametric and parametric methods of identification are analyzed. Selected methods are implemented in Matlab & Simulink sotware. Finally, the user guide for the created Python application is introduced.