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Title from title page of PDF (University of Missouri--St. Louis, viewed April 5, 2010). Includes bibliographical references (p. 84-89).

Neste trabalho apresentamos os modelos lineares parciais generalizados com uma variável explicativa contínua tratada de forma não paramétrica e os modelos lineares parciais aditivos generalizados com no mínimo duas variáveis explicativas contínuas tratadas de tal forma. São utilizados os P-splines para descrever a relação da variável resposta com as variáveis explicativas contínuas. Sendo assim, as funções de verossimilhança penalizadas, as funções escore penalizadas e as matrizes de informação de Fisher penalizadas são desenvolvidas para a obtenção das estimativas de máxima verossimilhança penalizadas por meio da combinação do algoritmo backfitting (Gauss-Seidel) e do processo iterativo escore de Fisher para os dois tipos de modelo. Em seguida, são apresentados procedimentos para a estimação do parâmetro de suavização, bem como dos graus de liberdade efetivos. Por fim, com o objetivo de ilustração, os modelos propostos são ajustados à conjuntos de dados reais. / In this work we present the generalized partial linear models with one continuous explanatory variable treated nonparametrically and the generalized additive partial linear models with at least two continuous explanatory variables treated in such a way. The P-splines are used to describe the relationship among the response and the continuous explanatory variables. Then, the penalized likelihood functions, penalized score functions and penalized Fisher information matrices are derived to obtain the penalized maximum likelihood estimators by the combination of the backfitting (Gauss-Seidel) algorithm and the Fisher escoring iterative method for the two types of model. In addition, we present ways to estimate the smoothing parameter as well as the effective degrees of freedom. Finally, for the purpose of illustration, the proposed models are fitted to real data sets.

Orientador: Paulo de Oliveira Camargo / Coorientador: Maurício Galo / Banca: João Carlos Chaves / Banca: Messias Meneguette Júnior / Banca: Sônia Maria Alves Costa / Banca: Silvio Jacks dos Anjos Garnés / Resumo: O avanço das técnicas de posicionamento, sobretudo do posicionamento por satélites artificiais, impulsionou os processos de atualização da estrutura geodésica fundamental em diversos países. No Brasil, a mais recente mudança foi a adoção do SIRGAS2000 em fevereiro de 2005, se tornando o terceiro referencial adotado oficialmente pelo Sistema Geodésico Brasileiro. A mudança de referencial faz com que produtos cartográficos possam ter suas coordenadas associadas a diferentes referenciais e/ou realizações. Portanto, tornam-se necessários processos de transformação de coordenadas entre sistemas e/ou realizações, que possam modelar as distorções existentes nas materializações e garantir a integridade dos dados. A evolução das técnicas de posicionamento e a atualização dos sistemas de referência são processos dinâmicos, portanto, os métodos para mudança de coordenadas são uma necessidade atual e constante. Nesta pesquisa é apresentado um método para transformação de coordenadas tridimensionais entre realizações de referenciais geodésicos com modelagem de distorções baseado em Thin-Plate Splines (TPS). Pretende-se explorar a capacidade da técnica TPS em modelar dados provenientes de uma transformação linear, juntamente com distorções de natureza... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The advances in the positioning techniques, especially in the satellite positioning, drove the updating process in the fundamental geodesic network in several countries. In Brazil, the most recent change was the adoption of the SIRGAS2000 in February, 2005, as the third official referential adopted by the Brazilian Geodetic System. The change of reference system let cartographic products with his coordinates associated to different reference systems and/or frames. So, processes of coordinates change between reference systems and/or frames are necessary, which are able to model the distortion in the reference frames and guarantee the data integrity. The evolution of the positioning techniques and the updating of reference frames are dynamic processes, so, the methods of coordinates change are an actual and continuous necessity. In this research a method is presented for transformation of threedimensional coordinates between reference frames with modeling of distortions based on Thin-Plate Splines (TPS). It is explored the capacity of the TPS in modeling data originated from a linear transformation, together... (Complete abstract click electronic access below) / Doutor

Made available in DSpace on 2014-06-12T23:14:42Z (GMT). No. of bitstreams: 2 arquivo2690_1.pdf: 6789118 bytes, checksum: 05cc3d356f57a760866a4fc16453b6e0 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Vários estudos têm indicado que, das várias classes de algoritmos de reconstrução aplicáveis para dados limitados, os baseados na técnica de reconstrução algébrica são mais flexíveis e precisos. Infelizmente, estas técnicas, geralmente, sofrem de ruídos ocasionados por processos de correção durante a reconstrução e também por inconsistências nos dados adquiridos pelos tomógrafos. O pós - processamento da imagem reconstruída com a aplicação de filtros pode ser feito para atenuar a presença de ruídos, mas geralmente atenuam também as descontinuidades presentes em bordas que distinguem objetos ou falhas. O presente trabalho propõe a redução de ruídos assegurando a continuidade (das derivadas) da superfície antes da reconstrução, representando cada incógnita por uma combinação linear de pontos de controle e suas bases B-splines. São aplicadas três bases Bsplines: B1 , B2 e B3, assegurando as continuidade C0, C1 e C2, respectivamente. Para validação da técnica, foram utilizadas simulações de modelos propostos na literatura e medidas experimentais por tomografia gama. Os resultados foram comparados com as técnicas algébricas ART, SIRT, MART e SMART, sendo validada satisfatoriamente para todos os phantoms propostos. Todas as bases B-splines aplicadas obtiveram erros menores que as técnicas de correção ART e SIRT, sendo a B3, a de melhor desempenho. Este resultado pode ser explicado pelas restrições de suavidade impostas à superfície reconstruída pelas bases Bsplines e a inclinação das técnicas aditivas a ruídos, principalmente para um número limitado de dados (5 e 10 vistas). A performance das técnicas multiplicativas para essa situação é a melhor, mostrando uma imagem sem artefatos e com pouco ruído. Devido a esse fato, a técnica b-spline não tem bons resultados, apresentando na maioria dos casos, erros maiores. Para todos os testes realizados, as técnicas de representação B-splines superaram os filtros de mesma natureza aplicados no pós-processamento, sugerindo que a técnica seja utilizada no lugar da filtragem pós-processamento

Imputation is one well recognized method for handling missing data. Multiple imputation provides a framework for imputing missing data that incorporate uncertainty about the imputations at the analysis stage. An important factor to consider when performing multiple imputation is the imputation model. In particular, a careful choice of the covariates to include in the model is crucial. The current recommendation by several authors in the literature (Van Buren, 2012; Moons et al., 2006, Little and Rubin, 2002) is to include all variables that will appear in the analytical model including the outcome as covariates in the imputation model. When the goal of the analysis is to explore the relationship between the outcome and the variable with missing data (the target variable), this recommendation seems questionable. Should we make use of the outcome to fill-in the target variable missing observations and then use these filled-in observations along with the observed data on the target variable to explore the relationship of the target variable with the outcome? We believe that this approach is circular. Instead, we have designed multiple imputation approaches rooted in machines learning techniques that avoid the use of the outcome at the imputation stage and maintain reasonable inferential properties. We also compare our approaches performances to currently available methods.

Indiana University-Purdue University Indianapolis (IUPUI) / Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.

Power system planning, reliability analysis and economically efficient capacity scheduling all rely heavily on electricity demand forecasting models. In the context of a deregulated wholesale electricity market, using scheduling a region’s bulk electricity generation is inherently linked to future values of demand. Predictive models are used by municipalities and suppliers to bid into the day-ahead market and by utilities in order to arrange contractual interchanges among neighboring utilities. These numerical predictions are therefore pervasive in the energy industry. This research seeks to develop a regression-based forecasting model. Specifically, electricity demand is modeled as a function of calendar effects, lagged demand effects, weather effects, and a stochastic disturbance. Variables such as temperature, wind speed, cloud cover and humidity are known to be among the strongest predictors of electricity demand and as such are used as model inputs. It is well known, however, that the relationship between demand and weather can be highly nonlinear. Rather than assuming a linear functional form, the structural change in these relationships is explored. Those variables that indicate a nonlinear relationship with demand are accommodated with penalized splines in a semiparametric regression framework. The equivalence between penalized splines and the special case of a mixed model formulation allows for model estimation with currently available statistical packages such as R, STATA and SAS. Historical data are available for the entire New England region as well as for the smaller zones that collectively make up the regional grid. As such, a secondary research objective of this thesis is to explore whether or not an aggregation of zonal forecasts might perform better than those produced from a single regional model. Prior to this research, neither the applicability of a semiparametric regression-based approach towards load forecasting nor the potential improvement in forecasting performance resulting from zonal load forecasting has been investigated for the New England wholesale electricity market.

A Logspline method of estimating an unknown density function f based on sample data is studied. Our approach is to use maximum likelihood estimation to estimate the unknown density function from a space of linear splines that have a finite number of fixed uniform knots. In the end of this thesis, the method is applied to a real survival data set of lung cancer patients.

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