Submitted by (ana.araujo@ufrpe.br) on 2016-05-25T16:16:39Z No. of bitstreams: 1 Cicero Carlos Felix de Oliveira.pdf: 934310 bytes, checksum: 4f4332b0b319f6bf33cdc1d615c36324 (MD5) / Made available in DSpace on 2016-05-25T16:16:39Z (GMT). No. of bitstreams: 1 Cicero Carlos Felix de Oliveira.pdf: 934310 bytes, checksum: 4f4332b0b319f6bf33cdc1d615c36324 (MD5) Previous issue date: 2011-07-08 / This dissertation is being dealt with a discrete distribution based on Bernoulli trials, which is the Negative Binomial distribution. The main objective is to propose a new non-informative prior distribution for the Negative Binomial model, which is being termed as a possible prior distribution Beta(0; 0), which is an improper distribution. This distribution is also known for the Binomial model as Haldane prior, but for the Negative Binomial model there are no studies to date. The study of the behavior of this prior was based on Bayesian and classical contexts. The idea of using a non-informative prior is the desire to make statistical inference based on the minimum of information prior subjective as possible. Well, makes it possible to compare the results of classical inference that uses only sample information, for example, the maximum likelihood estimator. When is compared the Beta(0; 0) distribution with the Bayes-Laplace prior and Jeffreys prior, based on the Bayesian estimators (posterior mean and posterior mode) and the maximum likelihood estimator, note that the possible Beta(0; 0) prior is less informative than the others prior. It is also verified that is prior possible is a limited distribution in parameter space, thus, an important feature for non-informative prior. The main argument shows that the possible Beta(0; 0) prior is adequate, when it is applied in a predictive posterior distribution for Negative Binomial model, leading the a Beta-Negative Binomial distribution (which corresponds the a hypergeometric multiplied by a probability). All observations citas are strengthened by several studies, such as: basic concepts related to Bayesian Inference and concepts of the negative binomial distribution and Beta-Negative Binomial (a mixture of Beta with the negative binomial) distribution. / Nesta dissertação está sendo abordado uma distribuição discreta baseada em ensaios de Bernoulli, que é a distribuição Binomial Negativa. O objetivo principal é prôpor uma nova distribuição a priori não informativa para o modelo Binomial Negativa, que está sendo denominado como uma possível distribuição a priori Beta(0; 0), que é uma distribuição imprópria. Essa distribuição também é conhecida para o modelo Binomial como a priori de Haldane, mas para o modelo Binomial Negativa não há nenhum estudo até o momento. O estudo do comportamento desta a priori foi baseada nos contextos bayesiano e clássico. A ideia da utilização de uma a priori não informativa é o desejo de fazer inferência estatística baseada no mínimo de informação subjetiva a priori quanto seja possível. Assim, torna possível a comparação com os resultados da inferência clássica que só usa informação amostral, como por exemplo, o estimador de máxima verossimilhança. Quando é comparado a distribuição Beta(0; 0) com a priori de Bayes - Laplace e a priori de Jeffreys, baseado-se nos estimadores bayesiano (média a posteriori e moda a posteriori) e no estimador de máxima verossimilhança, nota-se que a possível a priori Beta(0; 0) é menos informativa do que as outras a priori. É verificado também, que esta possível a priori é uma distribuição limitada no espaço paramétrico, sendo assim, uma característica importante para a priori não informativa. O principal argumento mostra que a possível a priori Beta(0; 0) é adequada, quando ela é aplicada numa distribuição a posteriori preditiva para modelo Binomial Negativa, levando a uma distribuição Beta Binomial Negativa (que corresponde a uma hipergeométrica multiplicada por uma probabilidade). Todas as observações citadas são fortalecidas por alguns estudos feitos, tais como: conceitos básicos associados à Inferência Bayesiana e conceitos das distribuições Binomial Negativa e Beta Binomial Negativa (que uma mistura da Beta com a Binomial Negativa).

A água de lastro de navios é um dos principais agentes dispersivos de organismos nocivos à saúde humana e ao meio ambiente e normas internacionais exigem que a concentração desses organismos no tanque seja menor que um valor previamente especificado. Por limitações de tempo e custo, esse controle requer o uso de amostragem. Sob a hipótese de que a concentração desses organismos no tanque é homogênea, vários autores têm utilizado a distribuição Poisson para a tomada de decisão com base num teste de hipóteses. Como essa proposta é pouco realista, estendemos os resultados para casos em que a concentração de organismos no tanque é heterogênea utilizando estratificação, processos de Poisson não-homogêneos ou assumindo que ela obedece a uma distribuição Gama, que induz uma distribuição Binomial Negativa para o número de organismos amostrados. Além disso, propomos uma nova abordagem para o problema por meio de técnicas de estimação baseadas na distribuição Binomial Negativa. Para fins de aplicação, implementamos rotinas computacionais no software R / Ballast water is a leading dispersing agent of harmful organisms to human health and to the environment and international standards require that the concentration of these organisms in the tank must be less than a prespecified value. Because of time and cost limitations, this inspection requires the use of sampling. Under the assumption of an homogeneous organism concentration in the tank, several authors have used the Poisson distribution for decision making based on hypothesis testing. Since this proposal is unrealistic, we extend the results for cases in which the organism concentration in the tank is heterogeneous, using stratification, nonhomogeneous Poisson processes or assuming that it follows a Gamma distribution, which induces a Negative Binomial distribution for the number of sampled organisms. Furthermore, we propose a novel approach to the problem through estimation techniques based on the Negative Binomial distribution. For practical applications, we implemented computational routines using the R software

Neste trabalho propomos a distribuição Weibull-Binomial-Negativa (WBN) considerando uma estrutura de ativação latente para explicar a ocorrência do evento de interesse, em que o número de causas competitivas é modelado pela distribuição Binomial Negativa, e os tempos não observados devido às causas seguem a distribuição Weibull. Em geral, as causas competitivas podem ter diferentes mecanismos de ativação, sendo assim os casos de primeira ativação, última ativação e ativação aleatória foram considerados no estudo. Desse modo o modelo proposto inclui uma ampla distribuição, tais como Weibull-Geométrico (WG) e Exponencial-Poisson Complementar (EPC), introduzidas por Barreto-Souza et al. (2011) e G. et al. (2011), respectivamente. Baseando-nos na mesma estrutura, consideramos o modelo de regressão locação-escala baseado na distribuição proposta (WBN) e o modelo para dados de sobrevivência com fração de cura. Os principais objetivos deste trabalho é estudar as propriedades matemáticas dos modelos propostos e desenvolver procedimentos de inferências desde uma perspectiva clássica e Bayesiana. Além disso, as medidas de diagnóstico Bayesiana baseadas na \'psi\'-divergência (Peng & Dey, 1995; Weiss, 1996), que inclui como caso particular a medida de divergência Kullback-Leibler (K-L), foram consideradas para detectar observações influentes / In this work we propose the Weibull-Negative-Binomial (WNB) considering a latent activation structure to explain the occurrence of an event of interest, where the number of competing causes are modeled by the Negative Binomial distribution and the no observed time due to the causes following the Weibull distribution. In general, the competitive causes may have different activation mechanisms, cases of first, last and random activation were considered in the study. Thus, the proposed model includes a wide distribution such as Weibull-Geometric distribution (WG) and Exponential-Poisson complementary (EPC) introduced by (Barreto-Souza et al., 2011) and (G. et al., 2011) respectively. Based on the same structure, we propose a location-scale regression model based on the proposed distribution (WNB) and the model for survival data with cure fraction. The main objectives of this work is to study the mathematical properties of the proposed models and develop procedures inferences from a classical and Bayesian perspective. Moreover, the Bayesian diagnostic measures based on the \'psi\'-divergence (Peng & Dey, 1995; Weiss, 1996), which includes Kullback-Leibler (K-L) divergence measure as a particular case, were considered to detect influential observations

No description available.

Metodologias para obtenção do tamanho amostral para estimar a concentração de organismos em água de lastro e verificar normas internacionais são desenvolvidas sob uma abordagem bayesiana. Consideramos os critérios da cobertura média, do tamanho médio e da minimização do custo total sob os modelos Poisson com distribuição a priori gama e binomial negativo com distribuição a priori Pearson Tipo VI. Além disso, consideramos um processo Dirichlet como distribuição a priori no modelo Poisson com o propósito de obter maior flexibilidade e robustez. Para fins de aplicação, implementamos rotinas computacionais usando a linguagem R. / Sample size methodologies for estimating the organism concentration in ballast water and for verifying international standards are developed under a Bayesian approach. We consider the criteria of average coverage, of average length and of total cost minimization under the Poisson model with a gamma prior distribution and the negative binomial model with a Pearson type VI prior distribution. Furthermore, we consider a Dirichlet process as a prior distribution in the Poisson model with the purpose to gain more flexibility and robustness. For practical applications, we implemented computational routines using the R language.

Recruitment of pelagic fish populations is believed to be regulated during the planktonic larval stage due to high rates of mortality during the early life stages. Starvation is thought to be one of the main sources of mortality, despite the fact that there is rarely a strong correlation between the feeding success of larval fish and food availability as measured in the field. This lack of relationship may be caused in part by (i) inadequate sampling of larval fish prey and (ii) the use of total zooplankton abundance or biomass as proxies for larval food availability. Many feeding studies rely on measures of average prey abundance which do not adequately capture the variability, or patchiness, of the prey field as experienced by larval fish. Previous studies have shown that larvae may rely on these patches to increase their feeding success. I assess the variability in the availability of larval fish prey over a range of scales and model the small-scale distribution of prey in Conception Bay, Newfoundland. I show that the greatest variability in zooplankton abundance existed at the meter scale, and that larval fish prey were not randomly distributed within the upper mixed layer. This will impact both how well we can model the stochastic nature of larval fish cohorts, as well as how well we can study larval fish feeding from gut content analyses. Expanding on six years of previous lab and field studies on larval Radiated Shanny (Ulvaria subbifurcata) from Conception Bay, Newfoundland, I assess the feeding success, niche breadth (S) and weight-specific feeding rates (SPC, d-1) of the larvae to determine whether there are size-based patterns evident across the years. I found that both the amount of food in the guts and the niche breadth of larvae increased with larval size. There was a shift from low to high SPC with increasing larval size, suggesting that foraging success increases as the larvae grow. My results suggest that efforts should be made to estimate the variability of prey abundance at scales relevant to larval fish foraging rather than using large-scale average abundance estimates, since small-scale prey patchiness likely plays a role in larval fish feeding dynamics. In addition, the characteristics of zooplankton (density, size and behaviour) should be assessed as not all zooplankton are preyed upon equally by all sizes of larval fish. Overall, this thesis demonstrates that indices based on averages fail to account for the variability in the environment and in individual larval fish, which may be confounding the relationship between food availability and larval growth.

Research Doctorate - Doctor of Philosophy (PhD) / Interval estimation of the Binomial parameter è, representing the true probability of a success, is a problem of long standing in statistical inference. The landmark work is by Bayes (1763) who applied the uniform prior to derive the Beta posterior that is the normalised Binomial likelihood function. It is not well known that Bayes favoured this ‘noninformative’ prior as a result of considering the observable random variable x as opposed to the unknown parameter è, which is an important difference. In this thesis we develop additional arguments in favour of the uniform prior for estimation of è. We start by describing the frequentist and Bayesian approaches to interval estimation. It is well known that for common continuous models, while different in interpretation, frequentist and Bayesian intervals are often identical, which is directly related to the existence of a pivotal quantity. The Binomial model, and its Poisson sister also, lack a pivotal quantity, despite having sufficient statistics. Lack of a pivotal quantity is the reason why there is no consensus on one particular estimation method, more so than its discreteness: frequentist (unconditional) coverage depends on è. Exact methods guarantee minimum coverage to be at least equal to nominal and approximate methods aim for mean coverage to be close to nominal. We agree with what seems like the majority of frequentists, that exact methods are too conservative in practice, and show additional undesirable properties. This includes more recent ‘short’ exact intervals. We argue that Bayesian intervals based on noninformative priors are preferable to the family of frequentist approximate intervals, some of which are wider than exact intervals for particular data values. A particular property of the interval based on the uniform prior is that its mean coverage is exactly equal to nominal. However, once committed to the Bayesian approach there is no denying that the current preferred choice, by ‘objective’ Bayesians, is the U-shaped Jeffreys prior which results from various methods aimed at finding noninformative priors. The most successful such method seems to be reference analysis which has led to sensible priors in previously unsolved problems, concerning multiparameter models that include ‘nuisance’ parameters. However, we argue that there is a class of models for which the Jeffreys/reference prior may be suboptimal and that in the case of the Binomial distribution the requirement of a uniform prior predictive distribution leads to a more reasonable ‘consensus’ prior.

Recruitment of pelagic fish populations is believed to be regulated during the planktonic larval stage due to high rates of mortality during the early life stages. Starvation is thought to be one of the main sources of mortality, despite the fact that there is rarely a strong correlation between the feeding success of larval fish and food availability as measured in the field. This lack of relationship may be caused in part by (i) inadequate sampling of larval fish prey and (ii) the use of total zooplankton abundance or biomass as proxies for larval food availability. Many feeding studies rely on measures of average prey abundance which do not adequately capture the variability, or patchiness, of the prey field as experienced by larval fish. Previous studies have shown that larvae may rely on these patches to increase their feeding success. I assess the variability in the availability of larval fish prey over a range of scales and model the small-scale distribution of prey in Conception Bay, Newfoundland. I show that the greatest variability in zooplankton abundance existed at the meter scale, and that larval fish prey were not randomly distributed within the upper mixed layer. This will impact both how well we can model the stochastic nature of larval fish cohorts, as well as how well we can study larval fish feeding from gut content analyses. Expanding on six years of previous lab and field studies on larval Radiated Shanny (Ulvaria subbifurcata) from Conception Bay, Newfoundland, I assess the feeding success, niche breadth (S) and weight-specific feeding rates (SPC, d-1) of the larvae to determine whether there are size-based patterns evident across the years. I found that both the amount of food in the guts and the niche breadth of larvae increased with larval size. There was a shift from low to high SPC with increasing larval size, suggesting that foraging success increases as the larvae grow. My results suggest that efforts should be made to estimate the variability of prey abundance at scales relevant to larval fish foraging rather than using large-scale average abundance estimates, since small-scale prey patchiness likely plays a role in larval fish feeding dynamics. In addition, the characteristics of zooplankton (density, size and behaviour) should be assessed as not all zooplankton are preyed upon equally by all sizes of larval fish. Overall, this thesis demonstrates that indices based on averages fail to account for the variability in the environment and in individual larval fish, which may be confounding the relationship between food availability and larval growth.

Cette thèse étudie le traitement statistique des images TEP. Plus particulièrement, la distribution binomiale négative est proposée pour modéliser l’activité d’une région mono-tissulaire. Cette représentation a l’avantage de pouvoir prendre en compte les variations d’activité biologique (ou hétérogénéité) d’un même tissu. A partir de ces résultats, il est proposé de modéliser la distribution de l’image TEP entière comme un mélange spatialement cohérent de lois binomiales négatives. Des méthodes Bayésiennes sont considérées pour la segmentation d’images TEP et l’estimation conjointe des paramètres du modèle. La cohérence spatiale inhérente aux tissus biologiques est modélisée par un champ aléatoire de Potts-Markov pour représenter la dépendance locale entre les composantes du mélange. Un algorithme original de Monte Carlo par Chaîne de Markov (MCMC) est utilisé, faisant appel aux notions d’échantillonnage dans un espace Riemannien et d’opérateurs proximaux. L’approche proposée est appliquée avec succès à la segmentation de tumeurs en imagerie TEP. Cette méthode est ensuite étendue d’une part en intégrant au processus de segmentation des informations anatomiques acquises par tomodensitométrie (TDM), et d’autre part en traitant une série temporelle d’images correspondant aux différentes phases de respiration. Un modèle de mélange de distributions bivariées binomiale négative - normale est proposé pour représenter les images dynamiques TEP et TDM fusionnées. Un modèle Bayésien hiérarchique a été élaboré comprenant un champ de Potts-Markov à quatre dimensions pour respecter la cohérence spatiale et temporelle des images PET-TDM dynamiques. Le modèle proposé montre une bonne qualité d’ajustement aux données et les résultats de segmentation obtenus sont visuellement en concordance avec les structures anatomiques et permettent la délimitation et le suivi de la tumeur. / This thesis studies statistical image processing of PET images. More specifically, the negative binomial distribution is proposed to model the activity of a single tissue. This representation has the advantage to take into account the variations of biological activity (or heterogeneity) within a single tissue. Based on this, it is proposed to model the data of the entire PET image as a spatially coherent finite mixture of negative binomial distributions. Bayesian methods are considered to jointly perform the segmentation and estimate the model parameters. The inherent spatial coherence of the biological tissue is modeled by a Potts-Markov random field to represent the local dependence between the components of the mixture. An original Markov Chain Monte Carlo (MCMC) algorithm is proposed, based on sampling in a Riemannian space and proximal operators. The proposed approach is successfully applied to the segmentation of tumors in PET imaging. This method is further extended by incorporating anatomical information acquired by computed tomography (CT) and processing a time series of images corresponding to the phases of respiration. A mixture model of bivariate negative binomial - normal distributions is proposed to represent the dynamic PET and CT fused images. A hierarchical Bayesian model was developed including a four dimensional Potts-Markov field to enforce the spatiotemporal coherence of dynamic PET-CT images. The proposed model shows a good fit to the data and the segmentation results obtained are visually consistent with the anatomical structures and allow accurate tumor delineation and tracking.

Les océans subissent des pressions d'origine anthropique particulièrement fortes comme la surpêche, la pollution physico-chimique, et le bruit rayonné par les activités industrielles et militaires. Cette thèse se place dans un contexte de compréhension de l'impact du bruit rayonné dans les océans sur les mammifères marins. L'acoustique passive joue donc un rôle fondamental dans ce problème. Ce travail aborde la tâche de détection de signatures acoustiques de mammifères marins dans le spectrogramme. Cette tâche est difficile pour deux raisons : 1. le bruit océanique a une structure complexe (non-stationnaire, coloré), 2. les signaux de mammifères marins sont inconnus et possèdent eux aussi une structure complexe (non-stationnaires bande étroite et/ou impulsionnels). Le problème doit donc être résolu de manière locale en temps-fréquence, et ne pas faire d'hypothèse a priori sur le signal. Des détecteurs statistiques basés uniquement sur la connaissance des statistiques du bruit dans le spectrogramme existent, mais souffrent deux lacunes : 1. leurs performances en terme de probabilité de fausse alarme/ probabilité de détection se dégradent fortement à faible rapport signal à bruit, et 2. ils ne sont pas capables de séparer les signaux à bande étroite des signaux impulsionnels. Ce travail apporte des pistes de réflexion sur ces problèmes.L'originalité de ce travail de thèse repose dans la formulation d'un test d'hypothèse binaire prenant explicitement en compte l'organisation spatiale des pics temps-fréquence. Nous introduisons une méthode d'Analyse de la Densité des Fausses Alarmes (FADA) qui permet de discriminer les régions temps-fréquence abritant le signal de celles n'abritant que du bruit. Plus précisément,le nombre de fausses alarmes dans une région du plan est d'abord modélisé par une loi binomiale, puis par une loi binomiale corrélée, afin de prendre en considération la redondance du spectrogramme. Le test d'hypothèse binaire est résolu par une approche de Neyman-Pearson. Nous démontrons numériquement la pertinence de cette approche et nous la validons sur données réelles de mammifères marins disposant d'une grande variété de signaux et de conditions de bruit. En particulier, nous illustrons la capacité de FADA à discriminer efficacement le signal du bruit en milieu fortement impulsionnel. / The oceans experience heavy anthropogenic pressure due to overfishing, physico-chemical pollution, and noise radiated by industrial and military activities. This work focuses on the use of passive acoustic monitoring of the oceans, as a tool to understand the impact of radiated noise on marine ecosystems, and particularly on marine mammals. This work tackles the task of detection of acoustical signals of marine mammals using the spectrogram. This task is uneasy for two reasons : 1. the ocean noise structure is complex (non-stationary and colored) and 2. the signals of interest are unknown and also shows a complex structure (non-stationary narrow band and/or impulsive). The problem therefore must be solved locally without making a priori hypothesis on the signal. Statistical detectors only based on the local analysis of the noise spectrogram coefficients are available, making them suitable for this problem. However, these detectors suffer two disadvantages : 1. the trade-offs false alarm probability/ detection probability that are available for low signal tonoise ratio are not satisfactory and 2. the separation between narrow-band and impulsive signals is not possible. This work brings some answers to these problems.The main contribution of this work is to formulate a binary hypothesis test taking explicitly in account the spatial organization of time-frequency peaks. We introduce the False Alarm Density Analysis (FADA) framework that efficiently discriminates time-frequency regions hosting signal from the ones hosting noise only. In particular the number of false alarms in regions of the binary spectrogram is first modeled by a binomial distribution, and then by a correlated binomial distribution to take in account the spectrogram redundancy. The binary hypothesis test is solved using a Neyman-Pearson criterion.We demonstrate the relevance of this approach on simulated data and validate the FADA detector on a wide variety of real signals. In particular we show the capability of the proposed method to efficiently detect signals in highly impulsive environment.