Essa dissertação procura analisar se as variações dos retornos de carteiras setoriais formadas por ações do Índice teórico da Bolsa de Valores de São Paulo (Ibovespa), do primeiro quadrimestre de 2010, podem ser explicadas pelo CAPM condicional não-paramétrico proposto por Wang (2002) e também por quatro variáveis de informação disponíveis aos investidores: (i) percentual de variação do nível de produção industrial brasileira; (ii) percentual de variação do monetário agregado M4; (iii) percentual de variação da inflação representada pelo Índice de Preços ao Consumidor Amplo (IPCA); e (iv) percentual de variação da taxa de câmbio real-dólar, obtida pela cotação do dólar PTAX. O estudo compreendeu as ações listadas na Bolsa de Valores de São Paulo no período de janeiro de 2002 a dezembro de 2009. Utilizou-se a metodologia de teste desenvolvida por Wang (2002) e replicada para o contexto mexicano por Castillo-Spíndola (2006). Foram utilizados os excessos de retornos mensais para as ações, carteiras e prêmio de mercado. Ainda, para estimar a influência das variáveis de informação, foram calculados seus respectivos percentuais de variação mensal, para o período de janeiro de 2002 a novembro de 2009. A fim de validar a aplicação do CAPM condicional não-paramétrico para o mercado acionário brasileiro, foram estimados os diversos parâmetros do modelo e testada sua validade estatística para cada variável de informação avaliando-se o p-value. Os resultados observados indicam que o modelo condicional não-paramétrico é relevante na explicação dos retornos das carteiras da amostra considerada para duas das quatro variáveis testadas, M4 e dólar PTAX. / This dissertation seeks to analyze if the variations of returns from sector portfolios, formed by shares of the São Paulo Stock Exchange Index (Ibovespa), in the first four months of 2010, could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM), suggested by Wang (2002), and also by four variables of information available to the investors: (i) percentage variation of the Brazilian industrial production level; (ii) percentage variation of broad money supply M4; (iii) percentage variation of the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA); and (iv) percentage variation in the real-dollar exchange rate, obtained by PTAX dollar quotation. This study comprised the shares listed in São Paulo Stock Exchange throughout January 2002 to December 2009. The test methodology developed by Wang (2002) and retorted to the Mexican context by Castillo-Spíndola (2006) was used. The excess of monthly returns for the shares, portfolios, and market premium were used. Still, aiming to estimate the influence of information variables, their monthly percentage variations were calculated for the period from January 2002 to November 2009. In order to validate the nonparametric conditional CAPM application for the Brazilian stock market, the models several parameters were estimated and its statistic validity was tested for each information variable, evaluating the p-value. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios returns of the sample considered for two among the four tested variables, M4 and PTAX dollar.

abstract: This investigation focuses on the development of uncertainty modeling methods applicable to both the structural and thermal models of heated structures as part of an effort to enable the design under uncertainty of hypersonic vehicles. The maximum entropy-based nonparametric stochastic modeling approach is used within the context of coupled structural-thermal Reduced Order Models (ROMs). Not only does this strategy allow for a computationally efficient generation of samples of the structural and thermal responses but the maximum entropy approach allows to introduce both aleatoric and some epistemic uncertainty into the system. While the nonparametric approach has a long history of applications to structural models, the present investigation was the first one to consider it for the heat conduction problem. In this process, it was recognized that the nonparametric approach had to be modified to maintain the localization of the temperature near the heat source, which was successfully achieved. The introduction of uncertainty in coupled structural-thermal ROMs of heated structures was addressed next. It was first recognized that the structural stiffness coefficients (linear, quadratic, and cubic) and the parameters quantifying the effects of the temperature distribution on the structural response can be regrouped into a matrix that is symmetric and positive definite. The nonparametric approach was then applied to this matrix allowing the assessment of the effects of uncertainty on the resulting temperature distributions and structural response. The third part of this document focuses on introducing uncertainty using the Maximum Entropy Method at the level of finite element by randomizing elemental matrices, for instance, elemental stiffness, mass and conductance matrices. This approach brings some epistemic uncertainty not present in the parametric approach (e.g., by randomizing the elasticity tensor) while retaining more local character than the operation in ROM level. The last part of this document focuses on the development of “reduced ROMs” (RROMs) which are reduced order models with small bases constructed in a data-driven process from a “full” ROM with a much larger basis. The development of the RROM methodology is motivated by the desire to optimally reduce the computational cost especially in multi-physics situations where a lack of prior understanding/knowledge of the solution typically leads to the selection of ROM bases that are excessively broad to ensure the necessary accuracy in representing the response. It is additionally emphasized that the ROM reduction process can be carried out adaptively, i.e., differently over different ranges of loading conditions. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2019

Essa dissertação procura analisar se as variações dos retornos de carteiras setoriais formadas por ações do Índice teórico da Bolsa de Valores de São Paulo (Ibovespa), do primeiro quadrimestre de 2010, podem ser explicadas pelo CAPM condicional não-paramétrico proposto por Wang (2002) e também por quatro variáveis de informação disponíveis aos investidores: (i) percentual de variação do nível de produção industrial brasileira; (ii) percentual de variação do monetário agregado M4; (iii) percentual de variação da inflação representada pelo Índice de Preços ao Consumidor Amplo (IPCA); e (iv) percentual de variação da taxa de câmbio real-dólar, obtida pela cotação do dólar PTAX. O estudo compreendeu as ações listadas na Bolsa de Valores de São Paulo no período de janeiro de 2002 a dezembro de 2009. Utilizou-se a metodologia de teste desenvolvida por Wang (2002) e replicada para o contexto mexicano por Castillo-Spíndola (2006). Foram utilizados os excessos de retornos mensais para as ações, carteiras e prêmio de mercado. Ainda, para estimar a influência das variáveis de informação, foram calculados seus respectivos percentuais de variação mensal, para o período de janeiro de 2002 a novembro de 2009. A fim de validar a aplicação do CAPM condicional não-paramétrico para o mercado acionário brasileiro, foram estimados os diversos parâmetros do modelo e testada sua validade estatística para cada variável de informação avaliando-se o p-value. Os resultados observados indicam que o modelo condicional não-paramétrico é relevante na explicação dos retornos das carteiras da amostra considerada para duas das quatro variáveis testadas, M4 e dólar PTAX. / This dissertation seeks to analyze if the variations of returns from sector portfolios, formed by shares of the São Paulo Stock Exchange Index (Ibovespa), in the first four months of 2010, could be explained by the nonparametric conditional Capital Asset Pricing Model (CAPM), suggested by Wang (2002), and also by four variables of information available to the investors: (i) percentage variation of the Brazilian industrial production level; (ii) percentage variation of broad money supply M4; (iii) percentage variation of the inflation represented by the Índice de Preços ao Consumidor Amplo (IPCA); and (iv) percentage variation in the real-dollar exchange rate, obtained by PTAX dollar quotation. This study comprised the shares listed in São Paulo Stock Exchange throughout January 2002 to December 2009. The test methodology developed by Wang (2002) and retorted to the Mexican context by Castillo-Spíndola (2006) was used. The excess of monthly returns for the shares, portfolios, and market premium were used. Still, aiming to estimate the influence of information variables, their monthly percentage variations were calculated for the period from January 2002 to November 2009. In order to validate the nonparametric conditional CAPM application for the Brazilian stock market, the models several parameters were estimated and its statistic validity was tested for each information variable, evaluating the p-value. The observed results indicate that the nonparametric conditional model is relevant in explaining the portfolios returns of the sample considered for two among the four tested variables, M4 and PTAX dollar.

<p>Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy. </p><p>The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics. </p><p>As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified. </p><p>In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community. </p><p>Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.</p>

Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy. The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics. As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified. In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community. Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.

Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2016-07-13T15:19:52Z No. of bitstreams: 2 Dissertação - Elvis Edgard Inga de La Cruz - 2015.pdf: 4690319 bytes, checksum: fb5f7a5ba3966e716246274700bab6ae (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-07-14T11:29:19Z (GMT) No. of bitstreams: 2 Dissertação - Elvis Edgard Inga de La Cruz - 2015.pdf: 4690319 bytes, checksum: fb5f7a5ba3966e716246274700bab6ae (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2016-07-14T11:29:19Z (GMT). No. of bitstreams: 2 Dissertação - Elvis Edgard Inga de La Cruz - 2015.pdf: 4690319 bytes, checksum: fb5f7a5ba3966e716246274700bab6ae (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2015-08-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This study aimed, expand the method of Production Systems Sustainability Perception Rice High -MPSAT Lands. The choice of this method is justified by the fact that it has a good theoretical framework and be easily understood by the actors; however, it lacks nonparametric statistical approach. They were interviewed actors in the production chain of upland rice in Mato Grosso on two occasions, in 2006/07, when the MPSATA and 2014 was developed as part of this research. The methodology transformed the qualitative elements that make up the MPSATA (questions, attributes, indicators, key variables, secondary counselors, primary guiding and sustainability dimensions) for ordinal type and identified the sense of the order of the categories of these elements. Then it verified the significance of matched elements, considering the 2007 and 2014 surveys, with the IBM-SPSS 21. Later the element values were calculated. These elements have as a data source practices and managements carried out along the production chain. The end result of the research was the development of Parametric Method Not for Perception Sustainability Highlands Rice Production System - MPSATA-NP. The results obtained by MPSATA-NP allowed to accept or reject in statistical terms, if there were significant differences between the values of the features found in different eras who were surveyed, revealing that the 221 questions elements, eleven have statistical difference between their medians (three environmental dimension, two of the socioeconomic scale, four in the economic dimension and two territorial dimension). The other sustainability factors have changes compared to paired terms, however, it can´t be said that these elements are in fact different and represent more or less sustainable production conditions. So it follows that not hear significant change in the perception of the actors with respect to the sustainability of the land rice production system in the region. / O presente trabalho teve como objetivo, ampliar o método da Percepção de Sustentabilidade de Sistemas de Produção Arroz de Terras Altas -MPSAT1. A escolha desse método se justifica pelo fato do mesmo possuir uma boa estrutura teórica e ser de fácil compreensão pelos atores, no entanto, carece de abordagem estatística não paramétrica. Foram entrevistados atores da cadeia produtiva de arroz de terras altas em Mato Grosso em dois momentos, em 2006/07, quando o MPSATA foi desenvolvido e 2014, como parte integrante da presente pesquisa. A metodologia utilizada transformou os elementos qualitativos que compõem o MPSATA (quesitos, atributos, indicadores, variáveis essenciais, orientadores secundários, orientadores primários e dimensões de sustentabilidade) para o tipo ordinal e identificou o sentido da ordem das categorias desses elementos. Em seguida foi verificada a significância dos elementos emparelhados, considerando os levantamentos de 2007 e 2014, com o software IBM-SPSS 21. Posteriormente foram calculados os valores dos elementos. Estes elementos possuem como fonte de dados as práticas e manejos realizados ao longo da cadeia produtiva. O resultado final da pesquisa foi a elaboração do Método Não Paramétrico para a Percepção de Sustentabilidade de Sistema de produção de Arroz de Terras Altas – MPSATA-NP. Os resultados obtidos pelo MPSATA-NP permitiram aceitar ou rejeitar em termos estatísticos, se havia diferenças significativa entre os valores dos elementos quesitos encontrados nas diferentes épocas que foram pesquisados, revelando que dos 221 elementos quesitos, onze possuem diferença estatística entre as suas medianas (três da dimensão ambiental, duas da dimensão socioeconômica, quatro da dimensão econômica e dois da dimensão territorial). Os outros elementos de sustentabilidade possuem mudanças, no entanto, não pode-se se afirmar sejam de fato diferentes e que representem condições de produção mais ou menos sustentáveis. Assim, se conclui que não ouve mudança significativa na percepção dos atores com respeito à sustentabilidade do sistema de produção de arroz de terras na região.

在過去研究犯罪經濟學的理論文獻上，失業率對各犯罪類型的影響為正向關係，但在實証文獻上的研究發現，卻有愈來愈多的証據支持此二個變數間的負向或無關係。為了解釋上述正向與負向間相反的矛盾關係，本篇論文嘗試利用兩種模型(非參數與非參數模型)與兩種效果(機會效果與動機效果)來解釋此二變數間的關係，此亦是本論文主要貢獻。其中機會效果是用以解釋失業率與犯罪間的負向關係，動機效果則用以解釋正向關係。在非參數模型中，利用失業率為景氣循環的代理變數，發現失業率與竊盜間存在正向關係，此與大多實証研究相符；失業率則和妨害風化與殺人犯罪間呈現負向相關；失業率與傷害罪間則沒有明顯正負關係。研究亦顯示，不同的犯罪類型在不同的參數模型下，統計的顯著性亦有不同，而在不同年齡層(青少年與成年人)的犯罪模型則更與理論模型結論相符。 / According to the theoretical literature on criminal economics, unemployment rate tends to be positively correlated to all types of crimes. However, more and more empirical evidence suggests otherwise. In order to clarify the relationship, this study exploits both nonparametric and parametric models and considers two effects, including opportunity and motivation effects. The presence of the opportunity effect leads to be a negative correlation between unemployment rate and crimes, while the presence of the motivation effect gives a positive correlation. Under nonparametric model where unemployment rate is used as a proxy for business cycles, we only found that there is positive correlation between unemployment rate and robbery, while obscenity and homicide are found to be negatively correlated with unemployment rate. This is in line with most empirical studies. Little correlation evidence is found for unemployment and other types of crimes. Under parametric model, the study indicates that the statistical significance differs in models, and depends on crime variable used. We found more consistent results with theoretic models for the age groups (teenagers and adults).

在迴歸分析中，若變數間具有非線性 (nonlinear) 的關係時，B-Spline線性迴歸是以無母數的方式建立模型。B-Spline函數為具有節點(knots)的分段多項式，選取合適節點的位置對B-Spline函數的估計有重要的影響，在希望得到B-Spline較好的估計量的同時，我們也想要只用少數的節點就達成想要的成效，於是Huang (2013) 提出了一種選擇節點的方式APLS (Adaptive penalized least squares)，在本文中，我們以此方法進行一些更一般化的設定，並在不同的設定之下，判斷是否有較好的估計效果，且已修正後的方法與基於BIC (Bayesian information criterion)的節點估計方式進行比較，在本文中我們將一般化設定的APLS法稱為GAPLS，並且經由模擬結果我們發現此兩種以B-Spline進行迴歸函數近似的方法其近似效果都很不錯，只是節點的個數略有不同，所以若是對節點選取的個數有嚴格要求要取較少的節點的話，我們建議使用基於BIC的節點估計方式，除此之外GAPLS法也是不錯的選擇。 / In regression analysis, if the relationship between the response variable and the explanatory variables is nonlinear, B-splines can be used to model the nonlinear relationship. Knot selection is crucial in B-spline regression. Huang (2013) propose a method for adaptive estimation, where knots are selected based on penalized least squares. This method is abbreviated as APLS (adaptive penalized least squares) in this thesis. In this thesis, a more general version of APLS is proposed, which is abbreviated as GAPLS (generalized APLS). Simulation studies are carried out to compare the estimation performance between GAPLS and a knot selection method based on BIC (Bayesian information criterion). The simulation results show that both methods perform well and fewer knots are selected using the BIC approach than using GAPLS.