This dissertation investigates the consequences of fractional dynamics for political modeling. Using Monte Carlo analyses, Chapters II and III investigate the threats to statistical inference posed by including fractionally integrated variables in bivariate and multivariate regressions. Fractional differencing is the most appropriate tool to guard against spurious regressions and other threats to inference. Using fractional differencing, multivariate models of British politics are developed in Chapter IV to compare competing theories regarding which subjective measure of economic evaluations best predicts support levels for the governing party; egocentric measures outperform sociotropic measures. The concept of fractional cointegration is discussed and the value of fractionally integrated error correction mechanisms are both discussed and demonstrated in models of Conservative party support. In Chapter V models of presidential approval in the United States are reconfigured in light of the possibilities of fractionally integrated variables. In both the British and American case accounting for the fractional character of all variables allows the development of more accurate multivariate models.

In September 1979, the Financial Accounting Standards Board (FASB) issued a statement entitled Financial Reporting and Changing Prices (FASB No. 33). FASB No. 33 requires publicly-held companies of a certain size to issue supplementary constant dollar and current cost disclosures along with their primary financial statements.To investigate the effect of the signals on security prices the study used a methodology known as "Iso-beta Portfolio Analysis" and employed different models in conjunction with the methodology, the market model (MM) and a new model called "the multi-index model" (MIM). Cluster analysis was used to develop the indexing used with the MIM.

[pt] Os métodos de análise de séries temporais têm se revelado uma importante ferramenta de apoio à tomada de decisões, com importância crescente em um mundo cada vez mais globalizado. Esse fato pode ser ilustrado, entre muitos outros, através de um convênio firmado entre o CEPEL, o Núcleo de Estatística Computacional da PUC/RJ e a Eletrobrás, para se avaliar a utilidade dessas ferramentas nas etapas do planejamento do setor elétrico brasileiro. A metodologia em Espaço de Estado proporcionou o surgimento de duas importantes classes de modelos de previsão e análise de séries temporais completamente alternativas (os modelos estruturais e os modelos de inovações em espaço de estado), e, por isso, podem por vezes, causar dúvidas quando se fala em métodos de previsão em espaço de estado sem se especificar sobre qual das duas se está falando. Foi escolhido uma técnica específica e facilmente executável em softwares comerciais para cada classe de modelos: O desenvolvimento clássico de Harvey implementado no software STAMP, representando os modelos estruturais; e o desenvolvimento de Goodrich implementado no software FMP, representando os modelos de inovações. Essas técnicas estão tratadas de uma forma aprofundada, para proporcionar um melhor entendimento teórico das diferenças existentes entre ambas. Com o intuito de se avaliar a performance frente às outras técnicas existentes, são comparados os resultados das previsões entre as metodologias a partir de um sistema de comparação baseado nas estatísticas MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Squared Error) e U-Theil. Para tanto são vistos sucintamente as técnicas: Alisamento Exponencial (Holt-Winters), Box & Jenkins e Redes Neurais. Todas as técnicas foram aplicadas aos dados de consumo de energia elétrica das 32 empresas concessionárias do setor no Brasil, além de comparadas com as previsões realizadas por essas concessionárias. A novidade deste trabalho para o projeto em andamento está na aplicação multivariada possível através da metodologia de Goodrich. / [en] The analysis of time series is, nowadays one of the most important tools in the decision making process, due mainly to the globalization of the world. As an illustration of that we can mention the recent contract signed between NEC/PUC-Rio and CEPEL/Eletrobrás, where time series techniques are to be used in the planning process of the brazilian sector. The state-space approach forms the basis of two important forecasting models to time series analysis the structural model and the state space innovation model. Because of that one finds it difficult to have a clear cut definition of either one of them. These two models formulation were implemented in comercial softwares: the structural model of A. Harvey in STAMP and the state space innovation of R. Goodrich in FMP. In order to check the perfomance of these state space approaches vis-à-vis the traditional forecasting techniques, it was used the following statistics: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Squared Error) and U-Theil. The traditional approaches used in the comparison were: Holt-Winters, Box & Jenkins and Backpropagation Neural Network. All the methods, included the state space ones were applied to the demand series of 32 electrical utilities which form the brazilian electrical distribution system. If was also attempted the multivariate state-space formulation of R. Goodrich which is included in FMP software.

abstract: Longitudinal studies contain correlated data due to the repeated measurements on the same subject. The changing values of the time-dependent covariates and their association with the outcomes presents another source of correlation. Most methods used to analyze longitudinal data average the effects of time-dependent covariates on outcomes over time and provide a single regression coefficient per time-dependent covariate. This denies researchers the opportunity to follow the changing impact of time-dependent covariates on the outcomes. This dissertation addresses such issue through the use of partitioned regression coefficients in three different papers. In the first paper, an alternative approach to the partitioned Generalized Method of Moments logistic regression model for longitudinal binary outcomes is presented. This method relies on Bayes estimators and is utilized when the partitioned Generalized Method of Moments model provides numerically unstable estimates of the regression coefficients. It is used to model obesity status in the Add Health study and cognitive impairment diagnosis in the National Alzheimer’s Coordination Center database. The second paper develops a model that allows the joint modeling of two or more binary outcomes that provide an overall measure of a subject’s trait over time. The simultaneous modelling of all outcomes provides a complete picture of the overall measure of interest. This approach accounts for the correlation among and between the outcomes across time and the changing effects of time-dependent covariates on the outcomes. The model is used to analyze four outcomes measuring overall the quality of life in the Chinese Longitudinal Healthy Longevity Study. The third paper presents an approach that allows for estimation of cross-sectional and lagged effects of the covariates on the outcome as well as the feedback of the response on future covariates. This is done in two-parts, in part-1, the effects of time-dependent covariates on the outcomes are estimated, then, in part-2, the outcome influences on future values of the covariates are measured. These model parameters are obtained through a Generalized Method of Moments procedure that uses valid moment conditions between the outcome and the covariates. Child morbidity in the Philippines and obesity status in the Add Health data are analyzed. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020

Autism spectrum disorder (ASD) is a neurodevelopment disorder characterized by impairment in communication skills, behavior and social interactions that affects around 1-2% worldwide. To date the etiology of ASD has not yet been fully understood, but in the last 18 years many advances have been made to understand the genetic component related to the development of the clinical phenotype. With the advent of genomic scan analysis such as chromosome analysis by microarray and whole exome sequencing (WHE) many advances have been made to understand the pathophysiology of the disease. About 10-15% of the cases can be explained by large losses or gains (deletions or duplications greater than 1000 base pairs) of the genetic material, which generally involve the disruption of one or more genes. Next generation sequencing methodologies were fundamental in the description of point mutations and small insertions and deletions associated with ASD. The WES has allowed many discoveries to be made about new candidate genes and mechanisms for the development of the disease. It is now claimed that de novo (non-inherited) and likely gene disruptive mutations, such as loss-of-function and non-synonymous changes with high prediction of damage by computational tools, in genes related to neurodevelopment are a major contributor to the disease mechanism. However, these mutations, in addition to not explaining the majority of cases, are rarely recurrent in the population, which makes it difficult to establish a definitive molecular diagnosis for most patients. WES is already a practice in clinical genetics laboratories and demonstrates high effectiveness for diseases that follow a Mendelian pattern of inheritance, and have an established genetic cause. In clinical practice WES is requested for cases of ASD, despite having different modes of inheritance and having more than 1,000 genes associated with the disease. Due to these characteristics the analysis of WES for ASD is a major challenge for the clinical laboratory. This study proposes the construction of a computerized WES analysis routine that can test different candidate genes for their sensitivity and specificity for the detection of affected individuals. The proposed approach consists in the counting of variants separated by their possible protein damage and population frequency for each individual from affected and control groups, this study analyzed 168 WES, being 49 with ASD and 119 controls. After counting formulation, these values are subjected to a sequence of statistical tests, seeking a significant difference in the amount of mutations of all the variants alone, loss-of-function or damaging missense mutations, and the application of models of multivariate analysis such as: logistic regression, decision tree, neural network, vector support machine and principal component analysis for the elaboration of more complex models for disease development. A total of 21 lists of genes were tested, of which 19 presented at least one significant result, and the analysis of variants alone was the one that obtained the largest number of significant events. From apparently protective variants (higher number in the control group), such as the missense variants in RAS/MAPK pathway as variants of stopgain with population frequency above 0.05 in chromatin genes in greater number in individuals with ASD. None of the multivariate analysis models had significant discrimination results between the two groups. Due to the small sample size, the results of this study should be interpreted with limitations, and it is necessary to replicate these scenarios in other databases. However, these findings suggest that different types and frequencies of variants may have distinct contributions to disease development depending on the genes analyzed, rather than complex relationships between variants of the same gene list / O transtorno do espectro autista (TEA) é um distúrbio do neurodesenvolvimento caracterizado por uma incapacidade de comunicação comportamento e interações sociais que afeta em torno de 1-2% da população mundial. Até o momento a etiologia do TEA ainda não é totalmente compreendida, mas nos últimos 18 anos muitos avanços foram feitos para entender o componente genético relacionado ao desenvolvimento do quadro clínico. Com o advento das análises de varredura genômica como a análise cromossômica por microarray e o sequenciamento completo de exoma (SCE) muitos avanços foram feitos para a compreensão da fisiopatologia da doença. Em torno de 10-15% dos casos podem ser explicados por grandes perdas ou ganhos (deleções ou duplicações superiores a 1000 pares de bases) do material genético, que geralmente envolvem a disrupção de um ou mais genes. As metodologias de sequenciamento de nova geração foram fundamentais para a descrição das mutações de ponto e pequenas inserções e deleções associadas ao TEA. O SCE permitiu que muitas descobertas fossem feitas sobre novos genes candidatos e mecanismos para o desenvolvimento da doença. Atualmente afirma-se que as alterações de novo (não herdadas) e de maior probabilidade de ruptura gênica, como as mutações de perda-de-função e as alterações não-sinônimas com alta predição de dano por ferramentas computacionais, em genes de susceptibilidade a doenças do neurodesenvolvimento sejam um grande contribuidor para o mecanismo da doença. Entretanto essas mutações, além de não explicar a totalidade dos casos raramente são recorrentes na população, o que dificulta o estabelecimento de um diagnóstico molecular definitivo para a maioria dos pacientes. O SCE já é uma prática nos laboratórios clínicos de genética e demonstra uma alta efetividade para as doenças que seguem um padrão de herança mendeliano, e têm uma causa genética estabelecida. Na prática clínica o SCE é solicitado para os casos de TEA, apesar de ter diferentes modos de herança e terem mais de 1,000 genes associados à doença. Devido a estas características o SCE para os casos de TEA são um grande desafio para o laboratório clínico. Este estudo propõem a construção de uma rotina computacional de análise do SCE que possa testar diferentes genes candidatos quanto à sua sensibilidade e especificidade para a detecção dos indivíduos afetados. A abordagem proposta é a contagem de variantes separadas por seu possível dano à proteína e frequência populacional para cada indivíduo de grupos afetado e controle em 168 indivíduos com SCE, sendo 49 com TEA e 119 controles. Após a formulação da contagem esses valores são submetidos a uma sequência de testes estatísticos, buscando diferença significativa em quantidade de mutações de todas as variantes isoladamente, das mutações de perda-de-função, ou não-sinônimas danosas como um conjunto e a aplicação de modelos de análise multivariada como: regressão logística, árvore de decisão, rede neural, máquinas de suporte de vetor e análise de componente principal para a elaboração de modelos mais complexos para o desenvolvimento na doença. Ao todo foram testadas 21 listas de genes, destas, 19 apresentaram ao menos um resultado significativo, sendo a análise de variantes isoladamente a que obteve maior número de eventos significativos. Desde variantes aparentemente protetoras (maior número no grupo controle), como as variantes não-sinônimas em via de RAS/MAPK quanto variantes de perda de códon de parada com frequência populacional acima de 0.05 em genes de cromatina em maior número nos indivíduos com TEA. Nenhum dos modelos de análise multivariada obteve resultados significativos na discriminação entre os dois grupos. Devido ao pequeno número amostral os resultados deste estudo devem ser interpretados com limitações, sendo necessária a replicação deste cenário em outros bancos de dados. Entretanto, estes achados sugerem que diferentes tipos e frequências de variantes podem ter contribuições distintas para o desenvolvimento da doença a depender dos genes analisados, mais de que relações complexas entre as variantes de uma mesma lista de genes

The availability of molecular markers covering the entire genome, such as single nucleotide polymorphism (SNP) markers, allied to the computational resources for processing large amounts of data, enabled the development of an approach for marker assisted selection for quantitative traits, known as genomic selection. In the last decade, genomic selection has been successfully implemented in a wide variety of animal and plant species, showing its benefits over traditional marker assisted selection and selection based only on pedigree information. However, some practical challenges may still limit the wide implementation of this method in a plant breeding program. For example, we cite the cost of high-density genotyping of a large number of individuals and the application of more complex models that take into account multiple traits and environments. Thus, this study aimed to i) investigate SNP calling and imputation strategies that allow cost-effective high-density genotyping, as well as ii) evaluating the application of multivariate genomic selection models to data from multiple traits and environments. This work was divided into two chapters. In the first chapter, we compared the accuracy of four imputation methods: NPUTE, Beagle, KNNI and FILLIN, using genotyping-by-sequencing (GBS) data from 1060 maize inbred lines, which were genotyped using different depths of coverage. In addition, two SNP calling and imputation strategies were evaluated. Our results indicated that combining SNP-calling and imputation strategies can enhance cost-effective genotyping, resulting in higher imputation accuracies. In the second chapter, multivariate genomic selection models, for multiple traits and environments, were compared with their univariate versions. We used data from 415 hybrids evaluated in the second season in four years (2006-2009) for grain yield, number of ears and grain moisture. Hybrid genotypes were inferred in silico based on their parental inbred lines using SNP markers obtained via GBS. However, genotypic information was available only for 257 hybrids, motivating the use of the H matrix, which combines genetic information based on pedigree and molecular markers. Our results demonstrated that the use of multi-trait multi-environment models can improve predictive abilities, especially to predict the performance of hybrids that have not yet been evaluated in any environment. / A disponibilidade de marcadores moleculares cobrindo todo o genoma, como os polimorfismos de nucleotídeos individuais (single nucleotide polymorphism - SNP), aliada aos recursos computacionais para o processamento de grande volume de dados, tornou possível o desenvolvimento de uma abordagem de melhoramento assistido para caracteres de herança quantitativa, conhecida como seleção genômica. Na última década a seleção genômica tem sido implementada com sucesso em uma enorme variedade de espécies animais e vegetais, comprovando suas vantagens sobre a seleção assistida por marcadores tradicional e a seleção baseada apenas em informações de parentesco. No entanto, alguns desafios práticos ainda podem limitar a implementação deste método em um programa de melhoramento de plantas. Como exemplos, citam-se o custo da genotipagem de alta densidade de um grande número de indivíduos e a aplicação de modelos mais complexos, que consideram múltiplos caracteres e ambientes. Dessa forma, este estudo teve como objetivos: i) investigar estratégias de identificação de SNPs e imputação que possibilitem uma genotipagem de alta densidade economicamente viável; e ii) avaliar a aplicação de modelos multivariados de seleção genômica para múltiplos caracteres e ambientes. Este trabalho foi divido em dois capítulos. No primeiro capítulo, comparou-se a acurácia de quatro métodos de imputação: NPUTE, Beagle, KNNI e FILLIN, usando dados de genotipagem por sequenciamento (genotyping-by-sequencing - GBS) de 1.060 linhagens de milho, que foram genotipadas usando diferentes profundidades de cobertura. Além disso, duas estratégias de identificação de SNPs e imputação foram avaliadas. Os resultados indicaram que a combinação de estratégias de detecção de polimorfismos e imputação pode possibilitar uma genotipagem economicamente viável, resultando em maiores acurácias de imputação. No segundo capítulo, modelos multivariados de seleção genômica, para múltiplos caracteres e ambientes, foram comparados com suas versões univariadas. Dados de 415 híbridos avaliados na segunda safra em quatro anos (2006-2009) para os caracteres produtividade de grãos, número de espigas e umidade foram utilizados. Os genótipos dos híbridos foram inferidos in silico com base nos genótipos das linhagens parentais usando marcadores SNPs obtidos via GBS. No entanto, informações genotípicas estavam disponíveis para apenas 257 híbridos, de modo que foi necessário fazer uso da matriz H, a qual combina informações de parentesco genético baseadas em pedigree e marcadores. Os resultados obtidos demonstraram que o uso de modelos de seleção genômica para múltiplos caracteres e ambientes pode aumentar a capacidade preditiva, especialmente para predizer a performance de híbridos nunca avaliados em qualquer ambiente.

Le travail présenté dans cette thèse a pour objectif de proposer un algorithme de classification supervisée d’images texturées basée sur la modélisation multivariée de champs texturaux. Inspiré des algorithmes de classification dits à « Sac de Mots Visuels » (SMV), nous proposons une extension originale au cas des descripteurs paramétriques issus de la modélisation multivariée des coefficients des sous-bandes d’une décomposition en ondelettes. Différentes contributions majeures de cette thèse peuvent être mises en avant. La première concerne l’introduction d’une loi a priori intrinsèque à l’espace des descripteurs par la définition d’une loi gaussienne concentrée. Cette dernière étant caractérisée par un barycentre ¯_ et une varianceσ2, nous proposons un algorithme d’estimation de ces deux quantités. Nous proposons notamment une application au cas des modèles multivariés SIRV ( Spherically Invariant Random Vector ), en séparant le problème complexe d’estimationdu barycentre comme la résolution de deux problèmes d’estimation plus simples ( un sur la partie gaussienne et un surle multiplieur ). Afin de prendre en compte la diversité naturelle des images texturées ( contraste, orientation, . . . ), nousproposons une extension au cas des modèles de mélanges permettant ainsi de construire le dictionnaire d’apprentissage.Enfin, nous validons cet algorithme de classification sur diverses bases de données d’images texturées et montrons de bonnes performances de classification vis-à-vis d’autres algorithmes de la littérature. / The prime objective of this thesis is to propose an unsupervised classification algorithm of textured images based on multivariate stochastic models. Inspired from classification algorithm named "Bag of Words" (BoW), we propose an original extension to parametric descriptors issued from the multivariate modeling of wavelet subband coefficients. Some major contributions of this thesis can be outlined. The first one concerns the introduction of an intrinsic prior on the parameter space by defining a Gaussian concentrated distribution. This latter being characterized by a centroid ¯_ and a variance _2,we propose an estimation algorithm for those two quantities. Next, we propose an application to the multivariate SIRV (Spherically Invariant Random Vector) model, by resolving the difficult centroid estimation problem as the solution of two simpler ones (one for the Gaussian part and one for the multiplier part). To handle with the intra-class diversity of texture images (scene enlightenment, orientation . . . ), we propose an extension to mixture models allowing the construction of the training dictionary. Finally, we validate this classification algorithm on various texture image databases and show interesting classification performances compared to other state-of-the-art algorithms.

Both traffic congestion and road accidents impose a burden on society, and it is therefore important for transport policy makers to reduce their impact. An ideal scenario would be that traffic congestion and accidents are reduced simultaneously, however, this may not be possible since it has been speculated that increased traffic congestion may be beneficial in terms of road safety. This is based on the premise that there would be fewer fatal accidents and the accidents that occurred would tend to be less severe due to the low average speed when congestion is present. If this is confirmed then it poses a potential dilemma for transport policy makers: the benefit of reducing congestion might be off-set by more severe accidents. It is therefore important to fully understand the relationship between traffic congestion and road accidents while controlling for other factors affecting road traffic accidents. The relationship between traffic congestion and road accidents appears to be an under researched area. Previous studies often lack a suitable congestion measurement and an appropriate econometric model using real-world data. This thesis aims to explore the relationship between traffic congestion and road accidents by using an econometric and GIS approach. The analysis is based on the data from the M25 motorway and its surrounding major roads for the period 2003-2007. A series of econometric models have been employed to investigate the effect of traffic congestion on both accident frequency (such as classical Negative Binomial and Bayesian spatial models) and accident severity (such as ordered logit and mixed logit models). The Bayesian spatial model and the mixed logit model are the best models estimated for accident frequency and accident severity analyses respectively. The model estimation results suggest that traffic congestion is positively associated with the frequency of fatal and serious injury accidents and negatively (i.e. inversely) associated with the severity of accidents that have occurred. Traffic congestion is found to have little impact on the frequency of slight injury accidents. Other contributing factors have also been controlled for and produced results consistent with previous studies. It is concluded that traffic congestion overall has a negative impact on road safety. This may be partially due to higher speed variance among vehicles within and between lanes and erratic driving behaviour in the presence of congestion. The results indicate that mobility and safety can be improved simultaneously, and therefore there is significant additional benefit of reducing traffic congestion in terms of road safety. Several policy implications have been identified in order to optimise the traffic flow and improve driving behaviour, which would be beneficial to both congestion and accident reduction. This includes: reinforcing electronic warning signs and the Active Traffic Management, enforcing average speed on a stretch of a roadway and introducing minimum speed limits in the UK. This thesis contributes to knowledge in terms of the relationship between traffic congestion and road accidents, showing that mobility and safety can be improved simultaneously. A new hypothesis is proposed that traffic congestion on major roads may increase the occurrence of serious injury accidents. This thesis also proposes a new map-matching technique so as to assign accidents to the correct road segments, and shows how a two-stage modelling process which combines both accident frequency and severity models can be used in site ranking with the objective of identifying hazardous accident hotspots for further safety examination and treatment.

[pt] A modelagem de dados que qualificam as transações de ativos financeiros, tais como, preço, spread de compra e venda, volume e duração, vem despertando o interesse de pesquisadores na área de finanças, levando a um aumento crescente do número de publicações referentes ao tema. As primeiras propostas se limitaram aos modelos de duração. Mais tarde, o impacto da duração sobre a volatilidade instantânea foi analisado. Recentemente, Manganelli (2002) incluiu dados referentes aos volumes transacionados dentro de um modelo vetorial. Neste estudo, nós estendemos o trabalho de Manganelli através da inclusão do spread de compra e venda num modelo vetorial autoregressivo, onde as médias condicionais do spread, volume, duração e volatilidade instantânea são descritas a partir de uma formulação exponencial chamada Exponential Multivariate Autoregressive Conditional Model (EMACM). Nesta nova proposta, não se fazem necessárias a adoção de quaisquer restrições nos parâmetros do modelo, o que facilita o procedimento de estimação por máxima verossimilhança e permite a utilização de testes de Razão de Verossimilhança na especificação da forma funcional do modelo (estrutura de interdependência). Em paralelo, a questão de antecipar movimentos nos preços de ativos financeiros é analisada mediante a utilização de um procedimento integrado, no qual, além da modelagem de dados financeiros de alta freqüência, faz-se uso de um modelo probit ordenado contemporâneo. O EMACM é empregado com o objetivo de capturar a dinâmica associada às variáveis e sua função de previsão é utilizada como proxy para a informação contemporânea necessária ao modelo de previsão de preços proposto. / [en] The availability of high frequency financial transaction data - price, spread, volume and duration -has contributed to the growing number of scientific articles on this topic. The first proposals were limited to pure duration models. Later, the impact of duration over instantaneous volatility was analyzed. More recently, Manganelli (2002) included volume into a vector model. In this document, we extended his work by including the bid-ask spread into the analysis through a vector autoregressive model. The conditional means of spread, volume and duration along with the volatility of returns evolve through transaction events based on an exponential formulation we called Exponential Multivariate Autoregressive Conditional Model (EMACM). In our proposal, there are no constraints on the parameters of the VAR model. This facilitates the maximum likelihood estimation of the model and allows the use of simple likelihood ratio hypothesis tests to specify the model and obtain some clues about the interdependency structure of the variables. In parallel, the problem of stock price forecasting is faced through an integrated approach in which, besides the modeling of high frequency financial data, a contemporary ordered probit model is used. Here, EMACM captures the dynamic that high frequency variables present, and its forecasting function is taken as a proxy to the contemporaneous information necessary to the pricing model.