Chemotherapy is a method used to treat cancer but it has a number of side-effects. Research conducted by the Department of Chemical Engineering at BYU involves a new method of administering chemotherapy using ultrasound waves and water-soluble capsules. The goal is to reduce the side-effects by localizing the delivery of the medication. As part of this research, a two-factor experiment was conducted on rats to test if the water-soluble capsules and ultrasound waves by themselves have an effect on tumor growth or patient survival. Our project emphasizes the usage of Bayesian Hierarchical Models and Win-BUGS to jointly model the survival data and the longitudinal data—mass. The results of the joint analysis indicate that the use of ultrasound and water-soluble microcapsules have no negative effect on survival. In fact, there appears to be a positive effect on the survival since the rats in the ultrasound-capsule group had higher survival rates than the rats in other treatment groups. From these results, it does appear that the new technology involving ultrasound waves and microcapsules is a promising way to reduce the side-effects of chemotherapy. It is strongly advocated that the formulation of a joint model for any longitudinal and survival data be performed. For future work for the ultrasound-microcapsule data it is recommended that joint modeling of the mass, tumor volume, and survival data be conducted to obtain additional information.

Meta-analysis is a tool used to combine the results from multiple studies into one comprehensive analysis. First developed in the 1970s, meta-analysis is a major statistical method in academic, medical, business, and industrial research. There are three traditional ways in which a meta-analysis is conducted: fixed or random effects, and using an empirical Bayesian approach. Derivations for conducting meta-analysis on correlations in the industrial psychology and organizational behavior (OB) discipline were reviewed by Hunter and Schmidt (2004). In this approach, Hunter and Schmidt propose an empirical Bayesian analysis where the results from previous studies are used as a prior. This approach is still widely used in OB despite recent advances in Bayesian methodology. This paper presents the results of a hierarchical Bayesian model for conducting meta-analysis of correlations and then compares these results to a traditional Hunter-Schmidt analysis conducted by Judge et al. (2001). In our approach we treat the correlations from previous studies as a likelihood, and present a prior distribution for correlations.

No description available.

Modeling the complex relationships between habitat characteristics and a species' habitat preferences pose many difficult problems for ecological researchers. These problems are complicated further when information is collected over a range of time or space. Additionally, the variety of factors affecting these choices is difficult to understand and even more difficult to accurately collect information about. In light of these concerns, we evaluate the performance of current standard habitat preference models that are based on Bayesian methods and then present some extensions and supplements to those methods that prove to be very useful. More specifically, we demonstrate the value of extending the standard Bayesian hierarchical model using finite mixture model methods. Additionally, we demonstrate that an extension of the Bayesian hierarchical changepoint model to allow for estimating multiple changepoints simultaneously can be very informative when applied to data about multiple habitat locations or species. These models allow the researcher to compare the sites or species with respect to a very specific ecological question and consequently provide definitive answers that are often not available with more commonly used models containing many explanatory factors. Throughout our work we use a complex data set containing information about horseshoe crab spawning habitat preferences in the Delaware Bay over a five-year period. These data epitomize some of the difficult issues inherent to studying habitat preferences. The data are collected over time at many sites, have missing observations, and include explanatory variables that, at best, only provide surrogate information for what researchers feel is important in explaining spawning preferences throughout the bay. We also looked at a smaller data set of freshwater mussel habitat selection preferences in relation to bridge construction on the Kennerdell River in Western Pennsylvania. Together, these two data sets provided us with insight in developing and refining the methods we present. They also help illustrate the strengths and weaknesses of the methods we discuss by assessing their performance in real situations where data are inevitably complex and relationships are difficult to discern. / Ph. D.

Selon le Groupe Intergouvernemental d'experts sur l'Évolution du Climat (GIEC), il est important de connaitre le climat passé afin de replacer le changement climatique actuel dans son contexte. Ainsi, de nombreux chercheurs ont travaillé à l'établissement de procédures permettant de reconstituer les températures ou les précipitations passées à l'aide d'indicateurs climatiques indirects. Ces procédures sont généralement basées sur des méthodes statistiques mais l'estimation des incertitudes associées à ces reconstructions reste une difficulté majeure. L'objectif principal de cette thèse est donc de proposer de nouvelles méthodes statistiques permettant une estimation précise des erreurs commises, en particulier dans le cadre de reconstructions à partir de données sur les cernes d'arbres.De manière générale, les reconstructions climatiques à partir de mesures de cernes d'arbres se déroulent en deux étapes : l'estimation d'une variable cachée, commune à un ensemble de séries de mesures de cernes, et supposée climatique puis l'estimation de la relation existante entre cette variable cachée et certaines variables climatiques. Dans les deux cas, nous avons développé une nouvelle procédure basée sur des modèles bayésiens semi- paramétriques. Tout d'abord, concernant l'extraction du signal commun, nous proposons un modèle hiérarchique semi-paramétrique qui offre la possibilité de capturer les hautes et les basses fréquences contenues dans les cernes d'arbres, ce qui était difficile dans les études dendroclimatologiques passées. Ensuite, nous avons développé un modèle additif généralisé afin de modéliser le lien entre le signal extrait et certaines variables climatiques, permettant ainsi l'existence de relations non-linéaires contrairement aux méthodes classiques de la dendrochronologie. Ces nouvelles méthodes sont à chaque fois comparées aux méthodes utilisées traditionnellement par les dendrochronologues afin de comprendre ce qu'elles peuvent apporter à ces derniers. / As stated by the Intergovernmental Panel on Climate Change (IPCC), it is important to reconstruct past climate to accurately assess the actual climatic change. A large number of researchers have worked to develop procedures to reconstruct past temperatures or precipitation with indirect climatic indicators. These methods are generally based on statistical arguments but the estimation of uncertainties associated to these reconstructions remains an active research field in statistics and in climate studies. The main goal of this thesis is to propose and study novel statistical methods that allow a precise estimation of uncertainties when reconstructing from tree-ring measurements data. Generally, climatic reconstructions from tree-ring observations are based on two steps. Firstly, a hidden environmental hidden variable, common to a collection of tree-ring measurements series, has to be adequately inferred. Secondly, this extracted signal has to be explained with the relevant climatic variables. For these two steps, we have opted to work within a semi-parametric bayesian framework that reduces the number of assumptions and allows to include prior information from the practitioner. Concerning the extraction of the common signal, we propose a model which can catch high and low frequencies contained in tree-rings. This was not possible with previous dendroclimatological methods. For the second step, we have developed a bayesian Generalized Additive Model (GAM) to explore potential links between the extracted signal and some climatic variables. This allows the modeling of non-linear relationships among variables and strongly differs from past dendrochronological methods. From a statistical perspective, a new selection scheme for bayesien GAM was also proposed and studied.

Em 2007 foi sancionada a Lei no 11.638, que possibilitou a adoção das Normas Internacionais de Relato Financeiro (International Financial Reporting Standards - IFRS) no Brasil. A convergência às IFRS trouxe diversas inovações, incluindo dentre elas a contabilização das aquisições de empresas, normatizadas pelo Pronunciamento Técnico CPC 15 - Combinações de negócio, que tornou obrigatória a aplicação do método de aquisição. Por esse novo modelo, a entidade adquirente contabiliza os ativos identificáveis adquiridos e os passivos assumidos da adquirida pelos seus respectivos valores justos. Já o ágio por expectativa de rentabilidade futura (goodwill) passa a ser mensurado pela parcela do valor justo da contraprestação transferida que não foi individualmente identificada, sendo realizado exclusivamente por meio do teste de impairment. Esta tese tem o objetivo de identificar fatores determinantes no percentual do custo de aquisição alocado como goodwill nas combinações de negócios realizadas e divulgadas pelas 101 empresas que compõe o Índice Brasil Amplo (IBr-A) durante o período entre 2009 e 2015. Foram identificados e coletados, em grande parte, manualmente, dados sobre 442 combinações de negócios, e utilizaram-se efetivamente as 307 observações em que foi reconhecido goodwill. Foi empregada uma técnica multivariada de dependência conhecida como modelos hierárquicos lineares ou modelos multinível (HLM), cuja característica basilar é a captura da estrutura aninhada dos dados, considerando a variabilidade dos dados dentro dos 39 segmentos econômicos das empresas da amostra deste estudo. Os resultados indicaram a existência de uma estrutura hierárquica, na qual o segmento econômico de atuação da adquirente explicou em torno de 15% da variabilidade no percentual do custo de aquisição alocado como goodwill. Durante a pesquisa foram testadas cinco variáveis relacionadas a fatores de 1o nível (percentual de remuneração variável da diretoria, relevância da aquisição, número de analistas, contraprestação em ações e o aproveitamento fiscal do goodwill) e outras duas relacionadas ao 2o nível (índice de imobilização e market-to-book ratio do segmento). Individualmente, dentre as variáveis de 1o nível, apenas o número de analistas acompanhando as ações da adquirente apresentou associação com o percentual de goodwill. As interações entre o aproveitamento fiscal e o índice de imobilização médio do segmento com o número de analistas também apresentaram associação. / In 2007, the approval of the Law 11.638 made the adoption of the International Accounting Reporting Standard (IFRS) possible in Brazil. The convergence to the IFRS brought several innovations, among then, the accounting for business combinations, regulated by the Pronunciamento Técnico CPC 15 - Combinações de negócios, which made the acquisition method mandatory. Under this new model, the acquirer measures the identifiable acquired assets and the assumed liabilities by their respective fair values. The goodwill, in turn, is the portion of the fair value of the consideration transferred that are not individually identified, and it is realized exclusively by the impairment test. This thesis aims to identify the determining factors in the percentage of the acquisition cost allocated as goodwill in the business combinations realized and disclosed by the 101 companies which comprises the Índice Brasil Amplo (IBr-A) in the period from 2009 to 2015. It was identified and, in great part, hand collected, data about 442 business combinations, and the 307 observationsin which a goodwill was recognized were effectively used. It was used a multivariate dependency technique known as Hierarchical Linear Models or Multilevel Models (HLM), thathas the fundamental characteristic of capturing the nested data structure, considering the variability within the 39 economic segments of the firms of this study\'s sample. The results indicated the presence of a hierarchical structure, in which the segment that the acquirer operates explains about 15% of the variability in the percentage of acquisition cost allocated as goodwill. During the research five first level variables were tested (variable remuneration percentage of the acquirer\'s directors, acquisition relevance, number of analysts, stock consideration and the goodwill tax allowance), as well as two related to the second level (properties, plants and equipment to total asset and market-to-book value of the segment). Individually, among the first level variables, only the number of analyst following the acquirer shares presented an association with the percentage of goodwill. The interactions between the use of the tax allowance and the properties, plants and equipment to total asset with the number of analyst also presented an association.

L’identification de systèmes dynamiques est une approche de modélisation fondée uniquement sur la connaissance des signaux d’entrée et de sortie de plus en plus utilisée en biologie. Dans ce même domaine d’application, des plans d’expériences sont souvent appliqués pour tester les effets de facteurs qualitatifs sur la réponse et chaque expérience est répétée plusieurs fois pour estimer la reproductibilité des résultats. Dans un objectif d’inférence, il est important de prendre en compte dans la procédure de modélisation les variabilités expliquées (effets fixes) et inexpliquées (effets aléatoires) entre les réponses individuelles. Une solution consiste à utiliser des modèles à effets mixtes mais jusqu’à présent il n’existe aucune approche similaire dans la communauté automaticienne de l’identification de systèmes. L’objectif de la thèse est de combler ce manque grâce à l’utilisation de structures de modèle hiérarchiques introduisant des effets mixtes au sein des représentations polynomiales boites noires de systèmes dynamiques linéaires. Une nouvelle méthode d’estimation des paramètres adaptée aussi bien à des structures simples comme ARX qu’à des structures plus complètes comme celle de Box-Jenkins est développée. Une solution au calcul de la matrice d’information de Fisher est également proposée. Finalement, une application à trois cas d’étude en biologie a permis de valider l’interêt pratique de l’approche d’identification de populations de systèmes dynamiques / System identification is a data-driven input-output modeling approach more and more used in biology and biomedicine. In this application context, methods of experimental design are often used to test effects of qualitative factors on the response and each assay is always replicated to estimate the reproducibility of outcomes. The inference of the modeling conclusions to the whole population requires to account within the modeling procedure for the explained variability (fixed effects) and the unexplained variabilities (random effects) between the individual responses. One solution consists in using mixed effects models but up to now no similar approach exists in the system identification literature. The objective of this thesis is to fill this gap by using hierarchical model structures introducing mixed effects within polynomial black-box representations of linear dynamical systems. A new method is developed to estimate parameters of model structures such as ARX or Box-Jenkins. A solution is also proposed to compute the Fisher’s matrix. Finally, three application studies are carried out and emphasize the practical relevance of the proposed approach to identify populations of dynamical systems

Em 2007 foi sancionada a Lei no 11.638, que possibilitou a adoção das Normas Internacionais de Relato Financeiro (International Financial Reporting Standards - IFRS) no Brasil. A convergência às IFRS trouxe diversas inovações, incluindo dentre elas a contabilização das aquisições de empresas, normatizadas pelo Pronunciamento Técnico CPC 15 - Combinações de negócio, que tornou obrigatória a aplicação do método de aquisição. Por esse novo modelo, a entidade adquirente contabiliza os ativos identificáveis adquiridos e os passivos assumidos da adquirida pelos seus respectivos valores justos. Já o ágio por expectativa de rentabilidade futura (goodwill) passa a ser mensurado pela parcela do valor justo da contraprestação transferida que não foi individualmente identificada, sendo realizado exclusivamente por meio do teste de impairment. Esta tese tem o objetivo de identificar fatores determinantes no percentual do custo de aquisição alocado como goodwill nas combinações de negócios realizadas e divulgadas pelas 101 empresas que compõe o Índice Brasil Amplo (IBr-A) durante o período entre 2009 e 2015. Foram identificados e coletados, em grande parte, manualmente, dados sobre 442 combinações de negócios, e utilizaram-se efetivamente as 307 observações em que foi reconhecido goodwill. Foi empregada uma técnica multivariada de dependência conhecida como modelos hierárquicos lineares ou modelos multinível (HLM), cuja característica basilar é a captura da estrutura aninhada dos dados, considerando a variabilidade dos dados dentro dos 39 segmentos econômicos das empresas da amostra deste estudo. Os resultados indicaram a existência de uma estrutura hierárquica, na qual o segmento econômico de atuação da adquirente explicou em torno de 15% da variabilidade no percentual do custo de aquisição alocado como goodwill. Durante a pesquisa foram testadas cinco variáveis relacionadas a fatores de 1o nível (percentual de remuneração variável da diretoria, relevância da aquisição, número de analistas, contraprestação em ações e o aproveitamento fiscal do goodwill) e outras duas relacionadas ao 2o nível (índice de imobilização e market-to-book ratio do segmento). Individualmente, dentre as variáveis de 1o nível, apenas o número de analistas acompanhando as ações da adquirente apresentou associação com o percentual de goodwill. As interações entre o aproveitamento fiscal e o índice de imobilização médio do segmento com o número de analistas também apresentaram associação. / In 2007, the approval of the Law 11.638 made the adoption of the International Accounting Reporting Standard (IFRS) possible in Brazil. The convergence to the IFRS brought several innovations, among then, the accounting for business combinations, regulated by the Pronunciamento Técnico CPC 15 - Combinações de negócios, which made the acquisition method mandatory. Under this new model, the acquirer measures the identifiable acquired assets and the assumed liabilities by their respective fair values. The goodwill, in turn, is the portion of the fair value of the consideration transferred that are not individually identified, and it is realized exclusively by the impairment test. This thesis aims to identify the determining factors in the percentage of the acquisition cost allocated as goodwill in the business combinations realized and disclosed by the 101 companies which comprises the Índice Brasil Amplo (IBr-A) in the period from 2009 to 2015. It was identified and, in great part, hand collected, data about 442 business combinations, and the 307 observationsin which a goodwill was recognized were effectively used. It was used a multivariate dependency technique known as Hierarchical Linear Models or Multilevel Models (HLM), thathas the fundamental characteristic of capturing the nested data structure, considering the variability within the 39 economic segments of the firms of this study\'s sample. The results indicated the presence of a hierarchical structure, in which the segment that the acquirer operates explains about 15% of the variability in the percentage of acquisition cost allocated as goodwill. During the research five first level variables were tested (variable remuneration percentage of the acquirer\'s directors, acquisition relevance, number of analysts, stock consideration and the goodwill tax allowance), as well as two related to the second level (properties, plants and equipment to total asset and market-to-book value of the segment). Individually, among the first level variables, only the number of analyst following the acquirer shares presented an association with the percentage of goodwill. The interactions between the use of the tax allowance and the properties, plants and equipment to total asset with the number of analyst also presented an association.

Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each individual and then doing ANOVA type analysis on the estimated parameters of the individual models is proposed and its power for different sample sizes and effect sizes is studied by simulation.

In this thesis, we address issues of model estimation for longitudinal categorical data and of model selection for these data with missing covariates. Longitudinal survey data capture the responses of each subject repeatedly through time, allowing for the separation of variation in the measured variable of interest across time for one subject from the variation in that variable among all subjects. Questions concerning persistence, patterns of structure, interaction of events and stability of multivariate relationships can be answered through longitudinal data analysis. Longitudinal data require special statistical methods because they must take into account the correlation between observations recorded on one subject. A further complication in analysing longitudinal data is accounting for the non- response or drop-out process. Potentially, the missing values are correlated with variables under study and hence cannot be totally excluded. Firstly, we investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from the Longitudinal Survey of Immigrants to Australia. Data for each subject is observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia. Secondly, we examine the Bayesian model selection techniques of the Bayes factor and Deviance Information Criterion for our regression models with miss- ing covariates. Computing Bayes factors involve computing the often complex marginal likelihood p(y|model) and various authors have presented methods to estimate this quantity. Here, we take the approach of path sampling via power posteriors (Friel and Pettitt, 2006). The appeal of this method is that for hierarchical regression models with missing covariates, a common occurrence in longitudinal data analysis, it is straightforward to calculate and interpret since integration over all parameters, including the imputed missing covariates and the random effects, is carried out automatically with minimal added complexi- ties of modelling or computation. We apply this technique to compare models for the employment status of immigrants to Australia. Finally, we also develop a model choice criterion based on the Deviance In- formation Criterion (DIC), similar to Celeux et al. (2006), but which is suitable for use with generalized linear models (GLMs) when covariates are missing at random. We define three different DICs: the marginal, where the missing data are averaged out of the likelihood; the complete, where the joint likelihood for response and covariates is considered; and the naive, where the likelihood is found assuming the missing values are parameters. These three versions have different computational complexities. We investigate through simulation the performance of these three different DICs for GLMs consisting of normally, binomially and multinomially distributed data with missing covariates having a normal distribution. We find that the marginal DIC and the estimate of the effective number of parameters, pD, have desirable properties appropriately indicating the true model for the response under differing amounts of missingness of the covariates. We find that the complete DIC is inappropriate generally in this context as it is extremely sensitive to the degree of missingness of the covariate model. Our new methodology is illustrated by analysing the results of a community survey.