Waterborne acoustic signals carry information about the ocean environment. Ocean geoacoustic inversion is the task of estimating environmental parameters from received acoustic signals by matching the measured sound with the predictions of a physics-based model. A lower bound on the uncertainty associated with environmental parameter estimates, the Cramér-Rao bound, can be calculated from the Fisher information, which is dependent on derivatives of a physics-based model. Physics-based preconditioners circumvent the need for variable step sizes when computing numerical derivatives. This work explores the feasibility of using a neural network to perform geoacoustic inversion for environmental parameters and their associated uncertainties from ship noise spectrogram data. To train neural networks, a synthetic dataset is generated and tested for generalizability against 31 measurements taken during the SBCEX2017 study of the New England Mud Patch.

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Em analise de dados que apresentam certo grau de assimetria a suposicao que as observações seguem uma distribuição normal, pode resultar ser uma suposição irreal e a aplicação deste modelo pode ocultar características importantes do modelo verdadeiro. Este tipo de situação deu forca á aplicação de modelo assimétricos, destacando-se entre estes a família de distribuições skew-symmetric, desenvolvida por Azzalini (1985). Neste trabalho nos apresentamos uma segunda proposta para a anàlise de dados com presença importante de assimetria e/ou curtose, comparado com a distribuição normal. Nós apresentamos e estudamos algumas propriedades dos modelos alfa-potência e log-alfa-potência, onde também estudamos o problema de estimação, as matrizes de informação observada e esperada de Fisher e o grau do viés dos estimadores mediante alguns processos de simulação. Nós introduzimos um modelo mais estável que o modelo alfa- potência do qual derivamos o caso bimodal desta distribuição e introduzimos os modelos bimodal simêtrico e assimêtrico alfa-potencia. Posteriormente nós estendemos a distribuição alfa-potência para o caso do modelo Birnbaum-Saunders, estudamos as propriedades deste novo modelo, desenvolvemos estimadores para os parametros e propomos estimadores com viés corrigido. Também introduzimos o modelo de regressão alfa-potência para dados censurados e não censurados e para o modelo de regressão log-linear Birnbaum-Saunders; aqui nós derivamos os estimadores dos parâmetros e estudamos algumas técnicas de validação dos modelos. Por ultimo nós fazemos a extensão multivariada do modelo alfa-potência e estudamos alguns processos de estimação dos parâmetros. Para todos os casos estudados apresentam-se ilustrações com dados já analisados previamente com outras suposições de distribuições. / In data analysis where data present certain degree of asymmetry the assunption of normality can result in an unreal situation and the application of this model can hide important caracteristics of the true model. Situations of this type has given strength to the use of asymmetric models with special emphasis on the skew-symmetric distribution developed by Azzalini (1985). In this work we present an alternative for data analysis in the presence of signi¯cant asymmetry or kurtosis, when compared with the normal distribution, as well as other situations that involve such model. We present and study of the properties of the ®-power and log-®-power distributions, where we also study the estimation problem, the observed and expected information matrices and the degree of bias in estimation using simulation procedures. A °exible model version is proposed for the ®-power distribution, following an extension to a bimodal version. Follows next an extension of the Birnbaum-Saunders distribution using the ®-power distribution, where some properties are studied, estimating approaches are developed as well as corrected bias estimator developed. We also develop censored and uncensored regression for the ®-power model and for the log-linear Birnbaum-Saunders regression models, for which model validation techniques are studied. Finally a multivariate extension of the ®-power model is proposed and some estimation procedures are investigated for the model. All the situations investigated were illustrated with data application using data sets previally analysed with other distributions.

Ce travail a été développé dans le cadre du projet européen FACETS-ITN, dans le domaine des Neurosciences Computationnelles. Son but est d’améliorer la compréhension des réseaux de neurones stochastiques de taille finie, pour des sources corrélées à caractère aléatoire et pour des matrices de connectivité biologiquement réalistes. Ce résultat est obtenu par l’analyse de la matrice de corrélation du réseau et la quantification de la capacité de codage du système en termes de son information de Fisher. Les méthodes comprennent diverses techniques mathématiques, statistiques et numériques, dont certaines ont été importés d’autres domaines scientifiques, comme la physique et la théorie de l’estimation. Ce travail étend de précédents résultats fondées sur des hypothèses simplifiées qui ne sont pas réaliste d’un point de vue biologique et qui peuvent être pertinents pour la compréhension des principes de travail liés cerveau. De plus, ce travail fournit les outils nécessaires à une analyse complète de la capacité de traitement de l’information des réseaux de neurones, qui sont toujours manquante dans la communauté scientifique. / The brain is the most complex system in the known universe. Its nested structure with small-world properties determines its function and behavior. The analysis of its structure requires sophisticated mathematical and statistical techniques. In this thesis we shed new light on neural networks, attacking the problem from different points of view, in the spirit of the Theory of Complexity and in terms of their information processing capabilities. In particular, we quantify the Fisher information of the system, which is a measure of its encoding capability. The first technique developed in this work is the mean-field theory of rate and FitzHugh-Nagumo networks without correlations in the thermodynamic limit, through both mathematical and numerical analysis. The second technique, the Mayer’s cluster expansion, is taken from the physics of plasma, and allows us to determine numerically the finite size effects of rate neurons, as well as the relationship of the Fisher information to the size of the network for independent Brownian motions. The third technique is a perturbative expansion, which allows us to determine the correlation structure of the rate network for a variety of different types of connectivity matrices and for different values of the correlation between the sources of randomness in the system. With this method we can also quantify numerically the Fisher information not only as a function of the network size, but also for different correlation structures of the system. The fourth technique is a slightly different type of perturbative expansion, with which we can study the behavior of completely generic connectivity matrices with random topologies. Moreover this method provides an analytic formula for the Fisher information, which is in qualitative agreement with the other results in this thesis. Finally, the fifth technique is purely numerical, and uses an Expectation-Maximization algorithm and Monte Carlo integration in order to evaluate the Fisher information of the FitzHugh-Nagumo network. In summary, this thesis provides an analysis of the dynamics and the correlation structure of the neural networks, confirms this through numerical simulation and makes two key counterintuitive predictions. The first is the formation of a perfect correlation between the neurons for particular values of the parameters of the system, a phenomenon that we term stochastic synchronization. The second, which is somewhat contrary to received opinion, is the explosion of the Fisher information and therefore of the encoding capability of the network for highly correlated neurons. The techniques developed in this thesis can be used also for a complete quantification of the information processing capabilities of the network in terms of information storage, transmission and modification, but this would need to be performed in the future.

Em analise de dados que apresentam certo grau de assimetria a suposicao que as observações seguem uma distribuição normal, pode resultar ser uma suposição irreal e a aplicação deste modelo pode ocultar características importantes do modelo verdadeiro. Este tipo de situação deu forca á aplicação de modelo assimétricos, destacando-se entre estes a família de distribuições skew-symmetric, desenvolvida por Azzalini (1985). Neste trabalho nos apresentamos uma segunda proposta para a anàlise de dados com presença importante de assimetria e/ou curtose, comparado com a distribuição normal. Nós apresentamos e estudamos algumas propriedades dos modelos alfa-potência e log-alfa-potência, onde também estudamos o problema de estimação, as matrizes de informação observada e esperada de Fisher e o grau do viés dos estimadores mediante alguns processos de simulação. Nós introduzimos um modelo mais estável que o modelo alfa- potência do qual derivamos o caso bimodal desta distribuição e introduzimos os modelos bimodal simêtrico e assimêtrico alfa-potencia. Posteriormente nós estendemos a distribuição alfa-potência para o caso do modelo Birnbaum-Saunders, estudamos as propriedades deste novo modelo, desenvolvemos estimadores para os parametros e propomos estimadores com viés corrigido. Também introduzimos o modelo de regressão alfa-potência para dados censurados e não censurados e para o modelo de regressão log-linear Birnbaum-Saunders; aqui nós derivamos os estimadores dos parâmetros e estudamos algumas técnicas de validação dos modelos. Por ultimo nós fazemos a extensão multivariada do modelo alfa-potência e estudamos alguns processos de estimação dos parâmetros. Para todos os casos estudados apresentam-se ilustrações com dados já analisados previamente com outras suposições de distribuições. / In data analysis where data present certain degree of asymmetry the assunption of normality can result in an unreal situation and the application of this model can hide important caracteristics of the true model. Situations of this type has given strength to the use of asymmetric models with special emphasis on the skew-symmetric distribution developed by Azzalini (1985). In this work we present an alternative for data analysis in the presence of signi¯cant asymmetry or kurtosis, when compared with the normal distribution, as well as other situations that involve such model. We present and study of the properties of the ®-power and log-®-power distributions, where we also study the estimation problem, the observed and expected information matrices and the degree of bias in estimation using simulation procedures. A °exible model version is proposed for the ®-power distribution, following an extension to a bimodal version. Follows next an extension of the Birnbaum-Saunders distribution using the ®-power distribution, where some properties are studied, estimating approaches are developed as well as corrected bias estimator developed. We also develop censored and uncensored regression for the ®-power model and for the log-linear Birnbaum-Saunders regression models, for which model validation techniques are studied. Finally a multivariate extension of the ®-power model is proposed and some estimation procedures are investigated for the model. All the situations investigated were illustrated with data application using data sets previally analysed with other distributions.

The Logistic Positive Exponent (LPE) model from Item Response Theory (IRT) and the Multistage Adaptive Testing (MST) using this model are the focus of this dissertation. For the LPE, item parameter estimations efficiency was studied, it was also analyzed the latent trait estimation for different response patterns to verify the effects it has on guessing and accidental mistakes. The LPE was put in contrast to Rasch, 2 and 3 parameter logistic models to compare the its efficiency. The item parameter estimations were implemented using the Bayesian approach for the Monte Carlo Markov Chain and the Marginal Maximum Likelihood. The latent trait estimation were calculated by the Expected a Posterior method. A goodness of fit analysis were made using the Posterior Predictive model-check method and information statistics. In the MST perspective, the LPE was compared with the Rasch and 2 logistic models. Different tests were constructed using methods that uses optimization functions to select items from a bank. Three functions were chosen to this task: the Fisher and Kullback-Leibler informations and the Continuous Entropy Method. The results were obtained with simulated and real data, the latter was from a general science knowledge test calls General Science test and it was provided by the Educational Testing Service company. Results showed that the LPE might help individuals that made mistakes in earlier stage of the test, especially for easy items. However, the LPE requires a large individual sample and time to estimate the item parameters making it an expensive model. MST based on LPE can be dissolve the impact of accidental mistakes from high performance test takers depending of the item pool available and the way the test is constructed. The optimization function performance vary depending of the situation. / O modelo Logístico de Expoente Positivo (LPE) da Teoria de Resposta ao Item (IRT) e o Teste Adaptativo Multiestágio (MST) sob esse modelo são os focos desta tese. Para o LPE, a eficiência da estimações dos parâmetros dos itens foram estudados, também foi analisado como as estimativas dos parâmetros dos indivíduos foram influenciados por padrões de respostas contendo chutes ou erros acidentais. O LPE foi comparado com os modelos de Rasch, Logístico de 2 e 3 Parâmetros para verificar seu desempenho. A estimação dos parâmetros dos itens foi implementada usando Monte Carlo via cadeias de Markov sob a abordagem Bayesiana e a Máxima Verossimilhança Marginal. As estimações dos traços latentes foram calculadas através do Método da Esperança a Posteriori. A qualidade do ajuste dos modelos foram analisadas usando o método Posterior Predictive model-check e critério de informações. Sob o contexto do MST, o LPE foi comparado com os modelos de Rasch e Logístico de 2 Parâmetro. Os MSTs foram construídos usando diferentes funções de objetivas que selecionaram os itens de bancos para comporem os testes. Três funções foram escolhidas para esse trabalho: As informações de Fisher e Kullback-Leibler e o Continuous Entropy Method. Os resultados para dados simulados e reais foram obtidos, os dados reais eram consituídos de respostas a perguntas sob conhecimento científico de do General Science test que foram fornecidos pela empresa Educational Testing Service. Resultados mostraram que o LPE pode ajudar os indivíduos que cometeram erros acidentais nas primeiras perguntas do teste, especialmente para os itens fáceis. Entretanto, este modelo requer tempo e uma grande quantidade de amostras de indivíduos para calcular as estimativas dos parâmetros dos itens o que o torna um modelo caro. O MST sob o modelo LPE pode diminuir o impacto de erros acidentais cometidos por examinandos com alto desempenho dependendo dos itens disponíveis no banco e a forma de construção do MST. O desempenho das funções objetivas variaram de acordo com cada situação.

The Logistic Positive Exponent (LPE) model from Item Response Theory (IRT) and the Multistage Adaptive Testing (MST) using this model are the focus of this dissertation. For the LPE, item parameter estimations efficiency was studied, it was also analyzed the latent trait estimation for different response patterns to verify the effects it has on guessing and accidental mistakes. The LPE was put in contrast to Rasch, 2 and 3 parameter logistic models to compare the its efficiency. The item parameter estimations were implemented using the Bayesian approach for the Monte Carlo Markov Chain and the Marginal Maximum Likelihood. The latent trait estimation were calculated by the Expected a Posterior method. A goodness of fit analysis were made using the Posterior Predictive model-check method and information statistics. In the MST perspective, the LPE was compared with the Rasch and 2 logistic models. Different tests were constructed using methods that uses optimization functions to select items from a bank. Three functions were chosen to this task: the Fisher and Kullback-Leibler informations and the Continuous Entropy Method. The results were obtained with simulated and real data, the latter was from a general science knowledge test calls General Science test and it was provided by the Educational Testing Service company. Results showed that the LPE might help individuals that made mistakes in earlier stage of the test, especially for easy items. However, the LPE requires a large individual sample and time to estimate the item parameters making it an expensive model. MST based on LPE can be dissolve the impact of accidental mistakes from high performance test takers depending of the item pool available and the way the test is constructed. The optimization function performance vary depending of the situation. / O modelo Logístico de Expoente Positivo (LPE) da Teoria de Resposta ao Item (IRT) e o Teste Adaptativo Multiestágio (MST) sob esse modelo são os focos desta tese. Para o LPE, a eficiência da estimações dos parâmetros dos itens foram estudados, também foi analisado como as estimativas dos parâmetros dos indivíduos foram influenciados por padrões de respostas contendo chutes ou erros acidentais. O LPE foi comparado com os modelos de Rasch, Logístico de 2 e 3 Parâmetros para verificar seu desempenho. A estimação dos parâmetros dos itens foi implementada usando Monte Carlo via cadeias de Markov sob a abordagem Bayesiana e a Máxima Verossimilhança Marginal. As estimações dos traços latentes foram calculadas através do Método da Esperança a Posteriori. A qualidade do ajuste dos modelos foram analisadas usando o método Posterior Predictive model-check e critério de informações. Sob o contexto do MST, o LPE foi comparado com os modelos de Rasch e Logístico de 2 Parâmetro. Os MSTs foram construídos usando diferentes funções de objetivas que selecionaram os itens de bancos para comporem os testes. Três funções foram escolhidas para esse trabalho: As informações de Fisher e Kullback-Leibler e o Continuous Entropy Method. Os resultados para dados simulados e reais foram obtidos, os dados reais eram consituídos de respostas a perguntas sob conhecimento científico de do General Science test que foram fornecidos pela empresa Educational Testing Service. Resultados mostraram que o LPE pode ajudar os indivíduos que cometeram erros acidentais nas primeiras perguntas do teste, especialmente para os itens fáceis. Entretanto, este modelo requer tempo e uma grande quantidade de amostras de indivíduos para calcular as estimativas dos parâmetros dos itens o que o torna um modelo caro. O MST sob o modelo LPE pode diminuir o impacto de erros acidentais cometidos por examinandos com alto desempenho dependendo dos itens disponíveis no banco e a forma de construção do MST. O desempenho das funções objetivas variaram de acordo com cada situação.

Simple random sampling (SRS) is the most commonly used sampling design in data collection. In many applications (e.g., in fisheries and medical research) quantification of the variable of interest is either time-consuming or expensive but ranking a number of sampling units, without actual measurement on them, can be done relatively easy and at low cost. In these situations, one may use rank-based sampling (RBS) designs to obtain more representative samples from the underlying population and improve the efficiency of the statistical inference. In this thesis, we study the theory and application of the finite mixture models (FMMs) under RBS designs. In Chapter 2, we study the problems of Maximum Likelihood (ML) estimation and classification in a general class of FMMs under different ranked set sampling (RSS) designs. In Chapter 3, deriving Fisher information (FI) content of different RSS data structures including complete and incomplete RSS data, we show that the FI contained in each variation of the RSS data about different features of FMMs is larger than the FI contained in their SRS counterparts. There are situations where it is difficult to rank all the sampling units in a set with high confidence. Forcing rankers to assign unique ranks to the units (as RSS) can lead to substantial ranking error and consequently to poor statistical inference. We hence focus on the partially rank-ordered set (PROS) sampling design, which is aimed at reducing the ranking error and the burden on rankers by allowing them to declare ties (partially ordered subsets) among the sampling units. Studying the information and uncertainty structures of the PROS data in a general class of distributions, in Chapter 4, we show the superiority of the PROS design in data analysis over RSS and SRS schemes. In Chapter 5, we also investigate the ML estimation and classification problems of FMMs under the PROS design. Finally, we apply our results to estimate the age structure of a short-lived fish species based on the length frequency data, using SRS, RSS and PROS designs.

Dans cette thèse, nous associons mesures expérimentales et modélisation des données pour étudier l'émission spontanée d'émetteurs fluorescents en environnement nano-structuré. Le mémoire est organisé en deux parties.Dans la première partie, nous étudions le transfert d'énergie entre émetteurs fluorescents en environnement plasmonique et sur des distances micrométriques. Pour commencer, nous caractérisons le transfert d'énergie entre deux ensembles d'émetteurs situés en champ proche d'une surface d'argent. Nous déterminons ainsi la dépendance en distance du taux de transfert d'énergie sur des distances micrométriques. Nous couplons ensuite une boite quantique et une bille fluorescente à un nano-fil d'argent et nous étudions le transfert d’énergie entre ces deux émetteurs, distants de plusieurs micromètres. Nous démontrons notamment le clignotement corrélé de ces deux émetteurs grâce à l'étude de la fonction de corrélation de leur intensité de fluorescence.Dans la seconde partie, nous sondons les variations spatiales de densité locale d'états électromagnétiques induites par des environnements nano-structurés grâce à différentes techniques de microscopie à super-résolution. A l'aide d'un microscope à balayage, nous réalisons tout d’abord une étude en trois dimensions de l’interaction de champ proche entre une bille fluorescente et différentes antennes en silicium. Nous introduisons ensuite une technique stochastique permettant de déterminer expérimentalement la position et le taux d'amortissement de molécules uniques photo-activées, avec une précision de localisation de l'ordre de 10 nm. Enfin, nous utilisons l'information de Fisher afin d'estimer les bornes inférieures de l'erreur type des estimations de positions et de taux d'amortissement réalisées dans le cadre de mesures sur molécules uniques. / In this thesis, we perform experimental measurements and data modelling to investigate spontaneous emission of fluorescent emitters in nanostructured environments. The manuscript is organised into two main parts.In the first part, we study micrometre-range energy transfer between fluorescent emitters in plasmonic environments. First of all, we characterise plasmon-mediated energy transfer between ensembles of fluorescent emitters located in the near field of a silver film. We thus determine the distance dependence of the energy transfer rate over micrometre distances. We then couple a single quantum dot and a fluorescent nanobead to a silver nanowire and we study evidences of the energy transfer between the two emitters, separated by several micrometres. We notably demonstrate a correlated blinking of the two emitters through the study of the correlation function of their fluorescence intensity.In the second part, we probe sub-wavelength spatial variations of the local density of electromagnetic states induced by nanostructured environments by means of different super-resolution microscopy techniques. To start with, we perform a three-dimensional study of the near-field interaction between a fluorescent nanobead and different silicon nanoantennas using a scanning-probe microscope. We then introduce a stochastic technique to experimentally determine the position and the fluorescence decay rate of single photo-activated molecules, with a localisation precision of the order of 10 nm. Finally, we use the Fisher information to estimate lower bounds on the standard errors on position and decay rate estimates performed in the context of single-molecule microscopy.