Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. Hence, suitable heteroskedasticity-consistent (HC) and heteroskedasticity and autocorrelation consistent (HAC) estimators have been receiving attention in the econometric literature over the last 20 years. To apply these estimators in practice, an implementation is needed that preferably translates the conceptual properties of the underlying theoretical frameworks into computational tools. In this paper, such an implementation in the package sandwich in the R system for statistical computing is described and it is shown how the suggested functions provide reusable components that build on readily existing functionality and how they can be integrated easily into new inferential procedures or applications. The toolbox contained in sandwich is extremely flexible and comprehensive, including specific functions for the most important HC and HAC estimators from the econometric literature. Several real-world data sets are used to illustrate how the functionality can be integrated into applications. / Series: Research Report Series / Department of Statistics and Mathematics

Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions-most importantly for the empirical estimating functions-from which various types of sandwich estimators can be computed. / Series: Research Report Series / Department of Statistics and Mathematics

Robôs móveis dependem de dados provenientes de sensores para ter uma representação do seu ambiente. Porém, os sensores geralmente fornecem informações incompletas, inconsistentes ou imprecisas. Técnicas de fusão de sensores têm sido empregadas com sucesso para aumentar a precisão de medidas obtidas com sensores. Este trabalho propõe e investiga o uso de técnicas de inteligência artificial para fusão de sensores com o objetivo de melhorar a precisão e acurácia de medidas de distância entre um robô e um objeto no seu ambiente de trabalho, obtidas com diferentes sensores. Vários algoritmos de aprendizado de máquina são investigados para fundir os dados dos sensores. O melhor modelo gerado com cada algoritmo é chamado de estimador. Neste trabalho, é mostrado que a utilização de estimadores pode melhorar significativamente a performance alcançada por cada sensor isoladamente. Mas os vários algoritmos de aprendizado de máquina empregados têm diferentes características, fazendo com que os estimadores tenham diferentes comportamentos em diferentes situações. Objetivando atingir um comportamento mais preciso e confiável, os estimadores são combinados em comitês. Os resultados obtidos sugerem que essa combinação pode melhorar a confiança e precisão das medidas de distâncias dos sensores individuais e estimadores usados para fusão de sensores. / Mobile robots rely on sensor data to have a representation of their environment. However, the sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of artificial intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of a distance between a robot and an object in its work environment using measures obtained from different sensors. Several machine learning algorithms are investigated to fuse the sensors data. The best model generated with each algorithm are called estimator. It is shown that the employment of the estimators based on artificial intelligence can improve significantly the performance achieved by each sensor alone. The machine learning algorithms employed have different characteristics, causing the estimators to have different behaviour in different situations. Aiming to achieve more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can improve the reliability and accuracy of the distance measures by the individual sensors and estimators used for sensor fusion.

Submitted by Guilherme Aparecido Santos Aguilar null (guiaguilar@hotmail.com) on 2017-03-24T20:21:32Z No. of bitstreams: 1 dissertacao.pdf: 1514798 bytes, checksum: 2336853543dbc4bd478bff182d7bc837 (MD5) / Approved for entry into archive by Luiz Galeffi (luizgaleffi@gmail.com) on 2017-03-27T17:03:00Z (GMT) No. of bitstreams: 1 aguilar_gas_me_prud.pdf: 1514798 bytes, checksum: 2336853543dbc4bd478bff182d7bc837 (MD5) / Made available in DSpace on 2017-03-27T17:03:00Z (GMT). No. of bitstreams: 1 aguilar_gas_me_prud.pdf: 1514798 bytes, checksum: 2336853543dbc4bd478bff182d7bc837 (MD5) Previous issue date: 2017-03-06 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Devido aos inúmeros campos para aplicações na Análise de Sobrevivência, diferentes funções de risco são necessárias para modelar os mais diversos casos em estudo. Portanto, ao criar novas distribuições pode-se obter diferentes funções de risco com suas diferentes curvas, que podem ser utilizadas para diversos tipos de dados. Serão apresentadas três novas distribuições de probabilidade, criadas a partir de três diferentes métodos, sendo a Gama Exponenciada Estendida de Marshall Olkin, Gama Exponenciada Poisson Truncada no Zero e também a Gama Exponenciada Bivariada. Para as distribuições de probabilidade univariadas foram obtidos resultados probabilísticos, tais como o n-ésimo momento; r-ésimo momento de vida média residual; r-ésimo momento de vida média residual invertido; ordenação estocástica; entropias; desvios médios; curvas de Bonferroni e de Lorenz; assimetria, curtose e seus gráficos; estatísticas de ordem e parâmetro stress − strength. Em relação a distribuição Gama Exponenciada Bivariada foi encontrada sua função acumulada; função densidade; função marginal; função condicional e seu n-ésimo momento. Para as novas distribuições univariadas encontradas, também foram feitas simulações para diferentes valores de parâmetros com o intuito de verificar qual o melhor método de estimação, para cada parâmetro de cada distribuição. Os métodos utilizados foram: estimador de máxima verossimilhança, Mínimos Quadrados, Mínimos Quadrados Ponderados, Cramér-von-Mises, Anderson Darling, Anderson Darling -RT (cauda à direita), Anderson Darling - LT (cauda à esquerda), Anderson Darling - 2LT (cauda à esquerda de segunda ordem), Kolmogorov e também foi utilizado o método Bayesiano com priori Gama. Por último foram também realizadas aplicações em um banco de dados, uma para cada distribuição univariada, onde foi comparado o ajuste das novas distribuições propostas com outras já conhecidas na literatura. / Due to the many fields for applications in Survival Analysis, different hazard functions are needed to modelling the various case studies. Therefore, creating new distributions can obtains different hazard functions with different graphics, which can be used for various types of data. There will be presented three new probability distributions, created from three different methods, the Marshall Olkin Extendet Exponentiated Gamma, Poisson Zero Truncated Exponentiated Gamma and the Bivariate Exponentiated Gamma. For such univariate probability distributions it will be obtained some probabilistics results, like n-th time, rth moment of residual life, r-th moment of residual life inverted, stochastic ordering, entropies, mean deviation, Bonferroni and Lorenz curve, skewness, kurtosis, order statistics and stress-strength parameter. Regarding the Bivariate Gamma Exponentiated was found your acumulated and density function; marginal function; conditional function and it’s n-th moment. For the new univariate distributions found, were also made simulations for different parameter values in order to find the best estimation method for each parameter. The methods used were: maximum likelihood, ordinary least-squares, weighted least-squares, Cramér-von-Mises, Anderson Darling, Anderson Darling - RT (right-tail), Anderson Darling - LT (left-tail), Anderson Darling - 2LT (left-tail second order), Kolmogorov and bayesian estimator with the prior Gamma. Some techniques to compare the estimators were used. Finally, applications were also performed, one for each univariate distribution, where the adjustment of some proposed distributions in relation to the database was tested.

In this thesis, M-estimators with Ramsay¡¦s function used in robust regression theory for linear parametric regression problems will be generalized to nonparametric Ramsay fuzzy neural networks (RFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on incremental gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed RFNNs. Simulation results show that the RFNNs proposed in this thesis have good robustness against outliers.

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In this article, we combine two so far separate strands of the economic literature and argue that democratization leads to a real exchange rate appreciation. We test this hypothesis empirically for a sample of countries observed from 1980 to 2007 by combining a difference-in-difference approach with propensity score matching estimators. Our empirical results reveal a strong and significant finding: democratization causes real exchange rates to appreciate. Consequently, the ongoing process of democratization observed in many parts of the world is likely to reduce exchange rate distortions.

In a "publish-or-perish culture", the ranking of scientific journals plays a central role in assessing the performance in the current research environment. With a wide range of existing methods for deriving journal rankings, meta-rankings have gained popularity as a means of aggregating different information sources. In this paper, we propose a method to create a meta-ranking using heterogeneous journal rankings. Employing a parametric model for paired comparison data we estimate quality scores for 58 journals in the OR/MS/POM community, which together with a shrinkage procedure allows for the identification of clusters of journals with similar quality. The use of paired comparisons provides a flexible framework for deriving an aggregated score while eliminating the problem of missing data.

Robôs móveis dependem de dados provenientes de sensores para ter uma representação do seu ambiente. Porém, os sensores geralmente fornecem informações incompletas, inconsistentes ou imprecisas. Técnicas de fusão de sensores têm sido empregadas com sucesso para aumentar a precisão de medidas obtidas com sensores. Este trabalho propõe e investiga o uso de técnicas de inteligência artificial para fusão de sensores com o objetivo de melhorar a precisão e acurácia de medidas de distância entre um robô e um objeto no seu ambiente de trabalho, obtidas com diferentes sensores. Vários algoritmos de aprendizado de máquina são investigados para fundir os dados dos sensores. O melhor modelo gerado com cada algoritmo é chamado de estimador. Neste trabalho, é mostrado que a utilização de estimadores pode melhorar significativamente a performance alcançada por cada sensor isoladamente. Mas os vários algoritmos de aprendizado de máquina empregados têm diferentes características, fazendo com que os estimadores tenham diferentes comportamentos em diferentes situações. Objetivando atingir um comportamento mais preciso e confiável, os estimadores são combinados em comitês. Os resultados obtidos sugerem que essa combinação pode melhorar a confiança e precisão das medidas de distâncias dos sensores individuais e estimadores usados para fusão de sensores. / Mobile robots rely on sensor data to have a representation of their environment. However, the sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of artificial intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of a distance between a robot and an object in its work environment using measures obtained from different sensors. Several machine learning algorithms are investigated to fuse the sensors data. The best model generated with each algorithm are called estimator. It is shown that the employment of the estimators based on artificial intelligence can improve significantly the performance achieved by each sensor alone. The machine learning algorithms employed have different characteristics, causing the estimators to have different behaviour in different situations. Aiming to achieve more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can improve the reliability and accuracy of the distance measures by the individual sensors and estimators used for sensor fusion.

The estimation of standard errors is essential to statistical inference. Statistical variability is inherent within data, but is usually of secondary interest; still, some options exist to deal with this variability. One approach is to carefully model the covariance structure. Another approach is robust estimation. In this approach, the covariance structure is estimated from the data. White (1980) introduced a biased, but consistent, robust estimator. Long et al. (2000) added an adjustment factor to White's estimator to remove the bias of the original estimator. Through the use of simulations, this project compares restricted maximum likelihood (REML) with four robust estimation techniques: the Standard Robust Estimator (White 1980), the Long estimator (Long 2000), the Long estimator with a quantile adjustment (Kauermann 2001), and the empirical option of the MIXED procedure in SAS. The results of the simulation show small sample and asymptotic properties of the five estimators. The REML procedure is modelled under the true covariance structure, and is the most consistent of the five estimators. The REML procedure shows a slight small-sample bias as the number of repeated measures increases. The REML procedure may not be the best estimator in a situation in which the covariance structure is in question. The Standard Robust Estimator is consistent, but it has an extreme downward bias for small sample sizes. The Standard Robust Estimator changes little when complexity is added to the covariance structure. The Long estimator is unstable estimator. As complexity is introduced into the covariance structure, the coverage probability with the Long estimator increases. The Long estimator with the quantile adjustment works as designed by mimicking the Long estimator at an inflated quantile level. The empirical option of the MIXED procedure in SAS works well for homogeneous covariance structures. The empirical option of the MIXED procedure in SAS reduces the downward bias of the Standard Robust Estimator when the covariance structure is homogeneous.