A falta de alternativas ao modelo normal uni/multivariado já é um problema superado pois atualmente é possível encontrar inúmeros trabalhos que introduzem e desenvolvem generalizações da distribuição normal com relação `a assimetria, curtose e/ou multimodalidade (Branco e Arellano-Valle (2004), Genton (2004), Arellano-Valle et al. (2006)). No contexto dos modelos unidimensionais da Teoria da Resposta ao Item (TRI), Bazán (2005) percebeu esta realidade e introduziu uma classe denominada PANA (Probito Assimétrico - Normal Assimétrica) a qual permite modelar possíveis comportamentos assimétricos de um modelo (uma probabilidade) de resposta ao item bem como a especificação de uma distribuição normal assimétrica para os traços latentes (unidimensionais) a qual é utilizada no processo de estimação. Motivado pela necessidade de melhor representar os fenômenos da área psicométrica (Heinen, 1996, p. 105) e da atual disponibilidade de distribuições elípticas assimétricas cujas propriedades são tão convenientes quanto aquelas devidas `a distribuição normal, a proposta do presente trabalho é apresentar uma extensão do modelo K-dimensional de 3 Parâmetros Probito (Kd3PP) com vetores de traços latentes normalmente distribuídos para o caso t-Assimétrico, gerando, assim, o que denominamos modelo Kd3PP-tA. Nossa proposta, portanto, pode ser considerada como uma extensão do trabalho desenvolvido por Bazán (2005) tanto no sentido de extender a distribuição unidimensional assimétrica dos traços latentes para o caso multidimensional quanto no que conscerne em considerar o achatamento (curtose) da distribuição. Nossa proposta também pode ser vista como uma extensão do trabalho de Béguin e Glas (2001) no sentido de desenvolver o método de estimação bayesiana dos modelos multidimensionais da TRI via DAGS (Dados Aumentados com Amostrador de Gibbs) para o caso em que os vetores de traços latentes comportam-se segundo uma distribuição multivariada t-Assimétrica. No desenvolvimento deste trabalho nos deparamos com uma das principais dificuldades encontradas no processo de estimação e inferência dos modelos multidimensionais da TRI que é a falta de identificabilidade e, com a intenção de ampliar e desmistificar nossos conhecimentos sobre um assunto ainda pouco explorado na literatura da TRI, apresentamos um estudo bibliográfico sobre este tema tanto sob o contexto da inferência clássica quanto bayesiana. Com o intuito de identificar situações particulares em que o uso de uma distribuição normal assimétrica para os traços latentes seja de maior relevância para a estimação e inferência dos parâmetros de item, bem como outros parâmetros relacionados à distribuição dos traços latentes, algumas análises sobre conjuntos de dados simulados são desenvolvidas. Como conclusão destas análises, podemos dizer que há uma melhora superficial quando a informação sobre uma possível assimetria na distribuição dos traços latentes não é ignorada. Além disso, os resultados favoreceram a seleção dos modelos que consideram distribuições assimétricas para os traços latentes, principalmente quando são considerados os modelos que possibilitam a estimação dos parâmetros de localização e escala da distribuição dos vetores de traços latentes. Duas principais contribuições que consideramos de ordem prática, são: a análise e a interpretação de testes através da estimação de modelos uni e multidimensionais da TRI que consideram tanto distribuições simétricas quanto assimétricas para os vetores de traços latentes e a disponibilização de uma função escrita em códigos R e C++ para a estimação dos modelos apresentados e desenvolvidos no presente trabalho. / The lack of alternatives to the univariate or multivariate normal model has been already solved because actually it has been possible to find several works that introduce and develop generalizations of the normal distribution in relation to the asymmetry, kurtosis and/or multimodality (Branco e Arellano-Valle (2004), Genton (2004), Arellano-Valle et al. (2006). In the context of unidimensional models of the Item Response Theory (IRT), Baz´an (2005) observed this fact and introduced a class called PANA (Probito Assimétrico - Normal Assimétrica) which allows to take account for asymmetry in the shape of an item response model (probability) and the specification of a skew normal distribution for unidimensional latent traits which is used in the estimation process. Motivated by the need to better represent the phenomenon of psychometric area (Heinen, 1996, p. 105) and the current availability of skew elliptical distributions whose properties are as convenient as those due to normal distribution, the proposal of this work is to provide an extension of multidimensional 3 Parameters Probit model (Kd3PP) where latent traits vectors are normally distributed for the case of Skew-t distribution (Sahu et al., 2003), generating therefore what we call Kd3PP-St model. Our proposal, therefore, can be regarded as an extension of the work of Bazán (2005) in two ways: the first is extending the unidimensional skew normal distribution of latent traits to the multidimensional case and second in the sense to consider the flattening (kurtosis) of this distribution. Our proposal can also be seen as an extension of the work of B´eguin e Glas (2001) in the sense that we develop the Bayesian estimation method of the 3 parameters multidimensional item response model by DAGS (Augmentated Data with Gibbs sampling) for the case where the latent trait vectors behave according to a Skew-t multivariate distribution. In the development of this work we come across one of the main difficulties encountered in the process of estimation and inference of multidimensional IRT models which is the lack of identifiabilitie and, with the intent to demystify and expand our knowledge on a subject still little explored in the literature of the IRT, we present a bibliographical study on this subject both in the context of classical and Bayesian inference. In order to identify particular situations where the use of a skew normal distribution is more relevant to the estimation and inference of item parameters as well as other parameters related to the distribution of latent traits, some analyses on simulated data sets are developed. As results of these analyses, we can say that there is a modest improvement when information about a possible asymmetry in the distribution of latent traits is not ignored. Moreover, the results favored the selection of models that consider asymmetric distributions for latent traits, especially when models that enable the estimation of parameters of location and scale from this distribution are considered. Two main contributions that we consider of pratical interest are: analysis and interpretations of tests using unidimensional and multidimensional IRT models that consider both simetric and skewed distributions for the vectors of latent traits and a function written in R and C++ language program that is made disponible for the estimation of models treated in this work.

<p>The main result in this thesis is the generalisation of Bergman's diamond lemma for ring theory to power series rings. This generalisation makes it possible to treat problems in which there arise infinite descending chains. Several results in the literature are shown to be special cases of this diamond lemma and examples are given of interesting problems which could not previously be treated. One of these examples provides a general construction of a normed skew field in which a custom commutation relation holds.</p><p>There is also a general result on the structure of totally ordered semigroups, demonstrating that all semigroups with an archimedean element has a (up to a scaling factor) unique order-preserving homomorphism to the real numbers. This helps analyse the concept of filtered structure. It is shown that whereas filtered structures can be used to induce pretty much any zero-dimensional linear topology, a real-valued norm suffices for the definition of those topologies that have a reasonable relation to the multiplication operation.</p><p>The thesis also contains elementary results on degree (as of polynomials) functions, norms on algebras (in particular ultranorms), (Birkhoff) orthogonality in modules, and construction of semigroup partial orders from ditto quasiorders.</p>

The main result in this thesis is the generalisation of Bergman's diamond lemma for ring theory to power series rings. This generalisation makes it possible to treat problems in which there arise infinite descending chains. Several results in the literature are shown to be special cases of this diamond lemma and examples are given of interesting problems which could not previously be treated. One of these examples provides a general construction of a normed skew field in which a custom commutation relation holds. There is also a general result on the structure of totally ordered semigroups, demonstrating that all semigroups with an archimedean element has a (up to a scaling factor) unique order-preserving homomorphism to the real numbers. This helps analyse the concept of filtered structure. It is shown that whereas filtered structures can be used to induce pretty much any zero-dimensional linear topology, a real-valued norm suffices for the definition of those topologies that have a reasonable relation to the multiplication operation. The thesis also contains elementary results on degree (as of polynomials) functions, norms on algebras (in particular ultranorms), (Birkhoff) orthogonality in modules, and construction of semigroup partial orders from ditto quasiorders.

在市場上最常被用來評價抵押債務債券(Collateralized Debt Obligation, CDO)的分析方法即為應用大樣本同質性資產組合(Large Homogeneous Portfolio, LHP)假設之單因子關聯結構模型(One Factor Copula Model)。由過去文獻指出，自2008年起，抵押債務債券的商品結構已漸漸出現改變，而目前所延伸之各種單因子關聯結構模型在新型商品的評價結果中皆仍有改善空間。 在本文中使用標準化偏斜Student-t分配(Standardized Skew Student-t distribution, SSTD)取代傳統的高斯分配進行抵押債務債券之分券的評價，此分配擁有控制分配偏態與峰態的參數。但是與Student-t分配相同，SSTD同樣不具備穩定的摺積(convolution)性質，因此在評價過程中會額外消耗部分時間。而在實證分析中，以單因子SSTD關聯結構模型評價擔保債務債券新型商品之分券時得到了較佳的結果，並且比單因子高斯關聯結構模型擁有更多參數以符合實際需求。 / The most widely used method for pricing collateralized debt obligation(CDO) is the one factor copula model with Large Homogeneous Portfolio assumption. Based on the literature of discussing, the structure of CDO had been changed gradually since 2008. The effects for pricing new type CDO tranches in the current extended one factor copula models are still improvable. In this article, we substitute the Gaussian distribution with the Standardized Skew Student-t distribution(SSTD) for pricing CDO tranches, and it has the features of heavy-tail and skewness. However, similar to the Student-t distribution, the SSTD is not stable under convolution as well. For this reason, it takes extra time in the pricing process. The empirical analysis shows that the one factor SSTD copula model has a good effect for pricing new type CDO tranches, and furthermore it brings more flexibility to the one factor Gaussian copula model.

Αντικείμενο της διατριβής είναι η επίδειξη μεθόδων, που βρίσκουν εφαρμογή, τόσο ειδικότερα στην σχεδίαση πολύπλοκων ψηφιακών μικροηλεκτρονικών κυκλωμάτων μεγάλης ταχύτητας, για τηλεπικοινωνιακά δίκτυα οπτικών ινών, όσο και γενικότερα για την επίλυση θεμάτων χρονισμού, που προκύπτουν κατά την υλοποίηση πολύπλοκων ολοκληρωμένων συστημάτων πάνω σε chip. Όσον αφορά, τον χώρο των τηλεπικοινωνιακών κυκλωμάτων, παρουσιάζονται μέθοδοι, τόσο για την συνολική οργάνωση του ολοκληρωμένου κυκλώματος, όσο και για την κυκλωματική υλοποίηση λειτουργικών μονάδων κοινών σε τηλεπικοινωνιακά κυκλώματα, με απαιτήσεις υψηλής ταχύτητας, χαμηλής κατανάλωσης, και ταυτόχρονης συνύπαρξης πολλαπλών ρολογιών. Η επίδειξη των προτεινόμενων μεθόδων καθώς και η επαλήθευση της ορθότητά τους, πραγματοποιείται, μέσα από την υλοποίηση σε πυρίτιο, ενός πολύπλοκου τηλεπικοινωνιακού ολοκληρωμένου κυκλώματος, με υψηλές απαιτήσεις ταχύτητας λειτουργίας. Όσον αφορά, τον γενικότερο χώρο της σχεδίασης πολύπλοκων ολοκληρωμένων System-on-Chip (SoC), παρουσιάζονται μέθοδοι για την επίλυση προβλημάτων χρονισμού, στα σύγχρονα ψηφιακά ολοκληρωμένα κυκλώματα, που σχετίζονται με την διάδοση και τον πολλαπλασιασμό της συχνότητας του ρολογιού, στο εσωτερικό των κυκλωμάτων αυτών. Πιο συγκεκριμένα, παρουσιάζονται μέθοδοι που μπορούν να εφαρμοστούν, τόσο για την εξάλειψη της παρέκκλισης, μεταξύ των κόμβων των εσωτερικών ρολογιών, όσο και για την εξάλειψη της παρέκκλισης μεταξύ εξωτερικού και εσωτερικού ρολογιού, στα ολοκληρωμένα κυκλώματα. Όσον αφορά το δεύτερο, η συχνότητα του εσωτερικού ρολογιού δεν ταυτίζεται απαραίτητα με αυτήν του εξωτερικού, αλλά επιτρέπεται να έχει πολλαπλάσια τιμή από αυτήν. Για την ευθυγράμμιση του εσωτερικού με το εξωτερικό ρολόι, προτείνεται η συστηματική μέθοδος LCD-SMD, η οποία είναι κατάλληλη για χρήση σε ολοκληρωμένα όπου επικρατούν συνθήκες μακρύ οδηγού ρολογιού, παράγει εσωτερικό ρολόι πολλαπλάσιο του εξωτερικού με σταθερό 50% duty-cycle, έχει μικρό χρόνο κλειδώματος, και χρησιμοποιεί εξ’ ολοκλήρου ψηφιακές λογικές πύλες. Η επικύρωση της ορθότητας των προτεινόμενων μεθόδων για θέματα χρονισμού, γίνεται κατά ένα μέρος με υλοποίηση σε πυρίτιο, και κατά ένα άλλο μέρος με εξομοιώσεις. / This Thesis aims to demonstrate design methods that can be applied as much in the design of high complexity, high speed, digital integrated circuits for optical fiber networks, as more generally to resolve timing issues, arising during the implementation of integrated circuits (IC’s). Specifically, in this Thesis we present methods for the holistic organization of a digital integrated circuit (driven by the needs imposed by nowadays telecommunications area), as well as methods regarding circuit implementation of various common functional units in telecommunications circuits that require high speed, low power and multiple clocks. The proposed methods are demonstrated and validated through the silicon implementation of a complex telecom integrated circuit (SDH framer). The design of the here-above mentioned chips lie into the more general area of the complex integrated Systems-on-Chips (SoCs). The methods developed in the Thesis, concern the distribution and frequency multiplication of the clock signal, inside the chip. In particular, we address between others, methods to remove the skew between the internal clock nodes, as well as methods to remove the skew between the internal and external clock. The internal clock frequency is allowed to be a multiple of the external clock frequency. For the alignment of the internal with the external clock, the systematic open-loop method LCD-SMD has been proposed, which is applicable to IC’s with long clock driver conditions. Through this method, we accomplish the generation of an internal clock with multiple frequencies than the external, while preserving a constant 50% duty-cycle. The method results into a fast lock time, and employs only standard digital logic gates. The proposed methods are validated both by silicon implementation and by simulations.

Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu. / Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text]

Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text] / Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.

The Hall resistivity in ferromagnetic materials has an anomalous contribution proportional to the magnetization, which is defined as the anomalous Hall effect (AHE). Being a central topic in the study of ferromagnetic materials for many decades, the AHE was revived in recent years by generating many new understandings and phenomena, e.g. spin-Hall effect, topological insulators. The phase diagram of the AHE was shown recently to exhibit three distinct regions: a skew scattering region in the high conductivity regime, a scattering-independent normal metal regime, and an insulating regime. While the origin of the metallic regime scaling has been understood for many decades through the expected dependence of each contribution, the origin of the surprising scaling in the insulating regime was completely unexplained, leaving the primary challenge to the last step to understand fully the AHE. In this dissertation work we developed a theory to study the AHE in the disordered insulating regime, whose scaling relation is observed to be omega_xy^AH is proportional to omega_xx^(1.40∼1.75) in a large range of materials. This scaling is qualitatively different from the ones observed in metals. In the metallic regime where kFl > > 1, the linear response theory predicts that omega_xx is proportional to the quasi-particle lifetime tau, while omega_xy^AH scales as alpha*tau beta*tau^0, indicating that the upper limit of the scaling exponent is 1.0. Basing our theory on the phonon-assisted hopping mechanism and percolation theory, we derived a general formula for the anomalous Hall conductivity (AHC), and showed that the AHC scales with the longitudinal conductivity as omega_xy^AH ~ omega_xx^gamma with gamma predicted to be 1.33 <= gamma <= 1.76, quantitatively in agreement with the experimental observations. This scaling remains similar regardless of whether the hopping process is long range type (varible range hopping) or short range type (activation E3 hopping), or is influenced by interactions, i.e. Efros-Shklovskii (E-S) regime. Our theory completes the understanding of the AHE phase diagram in the insulating regime.

Nessa tese apresentamos três diferentes aplicações dos modelos Birnbaum-Saunders. No capítulo 2 introduzimos um novo método por função-núcleo não-paramétrico para a estimação de densidades assimétricas, baseado nas distribuições Birnbaum-Saunders generalizadas assimétricas. Funções-núcleo baseadas nessas distribuições têm a vantagem de fornecer flexibilidade nos níveis de assimetria e curtose. Em adição, os estimadores da densidade por função-núcleo Birnbaum-Saunders gene-ralizadas assimétricas são livres de viés na fronteira e alcançam a taxa ótima de convergência para o erro quadrático integrado médio dos estimadores por função-núcleo-assimétricas-não-negativos da densidade. Realizamos uma análise de dados consistindo de duas partes. Primeiro, conduzimos uma simulação de Monte Carlo para avaliar o desempenho do método proposto. Segundo, usamos esse método para estimar a densidade de três dados reais da concentração de poluentes atmosféricos. Os resultados numéricos favorecem os estimadores não-paramétricos propostos. No capítulo 3 propomos uma nova família de modelos autorregressivos de duração condicional baseados nas distribuições misturas de escala Birnbaum-Saunders (SBS). A distribuição Birnbaum-Saunders (BS) é um modelo que tem recebido considerável atenção recentemente devido às suas boas propriedades. Uma extensão dessa distribuição é a classe de distribuições SBS, a qual (i) herda várias das boas propriedades da distribuição BS, (ii) permite a estimação de máxima verossimilhança em uma forma eficiente usando o algoritmo EM, e (iii) possibilita a obtenção de um procedimento de estimação robusta, entre outras propriedades. O modelo autorregressivo de duração condicional é a família primária de modelos para analisar dados de duração de transações de alta frequência. A metodologia estudada aqui inclui estimação dos parâmetros pelo algoritmo EM, inferência para esses parâmetros, modelo preditivo e uma análise residual. Realizamos simulações de Monte Carlo para avaliar o desempenho da metodologia proposta. Ainda, avalia-mos a utilidade prática dessa metodologia usando dados reais de transações financeiras da bolsa de valores de Nova Iorque. O capítulo 4 trata de índices de capacidade do processo (PCIs), os quais são ferramentas utilizadas pelas empresas para determinar a qualidade de um produto e avaliar o desempenho de seus processos de produção. Estes índices foram desenvolvidos para processos cuja característica de qualidade tem uma distribuição normal. Na prática, muitas destas ca-racterísticas não seguem esta distribuição. Nesse caso, os PCIs devem ser modificados considerando a não-normalidade. O uso de PCIs não-modificados podemlevar a resultados inadequados. De maneira a estabelecer políticas de qualidade para resolver essa inadequação, transformação dos dados tem sido proposta, bem como o uso de quantis de distribuições não-normais. Um distribuição não-normal assimétrica o qual tem tornado muito popular em tempos recentes é a distribuição Birnbaum-Saunders (BS). Propomos, desenvolvemos, implementamos e aplicamos uma metodologia baseada em PCIs para a distribuição BS. Além disso, realizamos um estudo de simulação para avaliar o desempenho da metodologia proposta. Essa metodologia foi implementada usando o software estatístico chamado R. Aplicamos essa metodologia para um conjunto de dados reais de maneira a ilustrar a sua flexibilidade e potencialidade. / In this thesis, we present three different applications of Birnbaum-Saunders models. In Chapter 2, we introduce a new nonparametric kernel method for estimating asymmetric densities based on generalized skew-Birnbaum-Saunders distributions. Kernels based on these distributions have the advantage of providing flexibility in the asymmetry and kurtosis levels. In addition, the generalized skew-Birnbaum-Saunders kernel density estimators are boundary bias free and achieve the optimal rate of convergence for the mean integrated squared error of the nonnegative asymmetric kernel density estimators. We carry out a data analysis consisting of two parts. First, we conduct a Monte Carlo simulation study for evaluating the performance of the proposed method. Second, we use this method for estimating the density of three real air pollutant concentration data sets, whose numerical results favor the proposed nonparametric estimators. In Chapter 3, we propose a new family of autoregressive conditional duration models based on scale-mixture Birnbaum-Saunders (SBS) distributions. The Birnbaum-Saunders (BS) distribution is a model that has received considerable attention recently due to its good properties. An extension of this distribution is the class of SBS distributions, which allows (i) several of its good properties to be inherited; (ii) maximum likelihood estimation to be efficiently formulated via the EM algorithm; (iii) a robust estimation procedure to be obtained; among other properties. The autoregressive conditional duration model is the primary family of models to analyze high-frequency financial transaction data. This methodology includes parameter estimation by the EM algorithm, inference for these parameters, the predictive model and a residual analysis. We carry out a Monte Carlo simulation study to evaluate the performance of the proposed methodology. In addition, we assess the practical usefulness of this methodology by using real data of financial transactions from the New York stock exchange. Chapter 4 deals with process capability indices (PCIs), which are tools widely used by companies to determine the quality of a product and the performance of their production processes. These indices were developed for processes whose quality characteristic has a normal distribution. In practice, many of these characteristics do not follow this distribution. In such a case, the PCIs must be modified considering the non-normality. The use of unmodified PCIs can lead to inadequacy results. In order to establish quality policies to solve this inadequacy, data transformation has been proposed, as well as the use of quantiles from non-normal distributions. An asymmetric non-normal distribution which has become very popular in recent times is the Birnbaum-Saunders (BS) distribution. We propose, develop, implement and apply a methodology based on PCIs for the BS distribution. Furthermore, we carry out a simulation study to evaluate the performance of the proposed methodology. This methodology has been implemented in a noncommercial and open source statistical software called R. We apply this methodology to a real data set to illustrate its flexibility and potentiality.

Made available in DSpace on 2016-06-02T20:06:02Z (GMT). No. of bitstreams: 1 2390.pdf: 3256865 bytes, checksum: 7ad1acbefc5f29dddbaad3f14dbcef7c (MD5) Previous issue date: 2009-04-17 / Financiadora de Estudos e Projetos / Microarrays technologies are used to measure the expression levels of a large amount of genes or fragments of genes simultaneously in diferent situations. This technology is useful to determine genes that are responsible for genetic diseases. A common statistical methodology used to determine whether a gene g has evidences to diferent expression levels is the t-test which requires the assumption of normality for the data (Saraiva, 2006; Baldi & Long, 2001). However this assumption sometimes does not agree with the nature of the analyzed data. In this work we use the skew-normal distribution described formally by Azzalini (1985), which has the normal distribution as a particular case, in order to relax the assumption of normality. Considering a frequentist approach we made a simulation study to detect diferences between the gene expression levels in situations of control and treatment through the t-test. Another simulation was made to examine the power of the t-test when we assume an asymmetrical model for the data. Also we used the likelihood ratio test to verify the adequability of an asymmetrical model for the data. / Os microarrays são ferramentas utilizadas para medir os níveis de expressão de uma grande quantidade de genes ou fragmentos de genes simultaneamente em situações variadas. Com esta ferramenta é possível determinar possíveis genes causadores de doenças de origem genética. Uma abordagem estatística comumente utilizada para determinar se um gene g apresenta evidências para níveis de expressão diferentes consiste no teste t, que exige a suposição de normalidade aos dados (Saraiva, 2006; Baldi & Long, 2001). No entanto, esta suposição pode não condizer com a natureza dos dados analisados. Neste trabalho, será utilizada a distribuição normal assimétrica descrita formalmente por Azzalini (1985), que tem a distribuição normal como caso particular, com o intuito de flexibilizar a suposição de normalidade. Considerando a abordagem clássica, é realizado um estudo de simulação para detectar diferenças entre os níveis de expressão gênica em situações de controle e tratamento através do teste t, também é considerado um estudo de simulação para analisar o poder do teste t quando é assumido um modelo assimétrico para o conjunto de dados. Também é realizado o teste da razão de verossimilhança, para verificar se o ajuste de um modelo assimétrico aos dados é adequado.