Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on Apr. 23, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Renewable Resources, University of Alberta. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1996. / Vita. Includes bibliographical references (leaves [127]-133).

Thesis (Ph.D. in Nursing) -- University of Colorado, 2005. / Typescript. Includes bibliographical references (leaves 198-212). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;

Thesis (Ph. D.)--Illinois State University, 1999. / Title from title page screen, viewed July 24, 2006. Dissertation Committee: Kenneth H. Strand, Jeffrey B. Hecht (co-chairs), Paul J. Baker, William C. Rau. Includes bibliographical references (leaves 136-148) and abstract. Also available in print.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 105-107).

Environmental factors affect fish distribution and fisher behavior. These factors are seldom included in stock assessment models, resulting in potentially inaccurate fish abundance estimates. This study determined the impact of these factors using the commercial catch rate of sauger (Sander canadensis) and walleye (Sander vitreus) in Lake Winnipeg by: (1) the use of satellite data to monitor turbidity and its impact on catch via simple linear regression and (2) the effect of environment on catch and effort using generalized linear models. No statistically significant relationship was found between catch and turbidity; a result which may be due to small sample sizes, the fish species' examined, and variable turbidity at depth. Decreased effort was correlated with harsh weather and decreased walleye catch. Increased walleye catch was correlated with low temperature and low Red River discharge. Increased sauger catch was correlated with high temperature, high cloud opacity, and average Red River discharge. / October 2006

Σε αυτή την εργασία ασχοληθήκαμε με δύο μεθόδους, που σκοπός τους είναι να κατατάσσουν τις παρατηρήσεις σε γνωστές ομάδες και στη συνέχεια να κάνουν προβλέψεις για καινούριες παρατηρήσεις. Αυτές οι μέθοδοι είναι η λογιστική παλινδρόμηση (logistic regression) και η διαχωριστική ανάλυση (discriminant analysis). Στο πρώτο κεφάλαιο αναφέραμε περιληπτικά τα μη γραμμικά μοντέλα παλινδρόμησης (αφού και η λογιστική παλινδρόμηση είναι ένα τέτοιο μοντέλο). Απλά αναφέρουμε τη μορφή που έχουν αυτά τα μοντέλα, με ποιες μεθόδους μπορούμε να εκτιμήσουμε τις παραμέτρους παλινδρόμησης, ποια είναι τα διαστήματα εμπιστοσύνης για τους συντελεστές παλινδρόμησης και τη μορφή που θα έχουν οι έλεγχοι υποθέσεων. Στο δεύτερο κεφάλαιο περιγράφουμε τη λογιστική παλινδρόμηση. Η λογιστική παλινδρόμηση είναι χρήσιμη σε καταστάσεις στις οποίες επιθυμούμε να προβλέψουμε την ύπαρξη ή την απουσία ενός χαρακτηριστικού ή ενός συμβάντος. Η πρόβλεψη αυτή βασίζεται στην κατασκευή ενός μοντέλου και συγκεκριμένα στον προσδιορισμό των τιμών που παίρνουν οι συντελεστές. Αυτή η μέθοδος είναι μια γενίκευση της απλή γραμμικής παλινδρόμησης για την περίπτωση όπου η εξαρτημένη μεταβλητή είναι δίτιμη (παίρνει την τιμή 0 όταν το χαρακτηριστικό απουσιάζει και την τιμή 1 όταν υπάρχει το χαρακτηριστικό). Στο τρίτο κεφάλαιο αναλύουμε τη διαχωριστική ανάλυση, η οποία έχει δύο στόχους: να χωρίσει ένα πληθυσμό σε ευδιάκριτες ομάδες και με τη βοήθεια ενός διαχωριστικού κανόνα να κατατάσσει παρατηρήσεις στις ευδιάκριτες ομάδες. Στο τέλος του κεφαλαίου περιγράφουμε τις ομοιότητες και τις διαφορές της διαχωριστικής ανάλυσης και της λογιστικής παλινδρόμησης. Στο τέταρτο και τελευταίο κεφάλαιο απλά δίνουμε ένα παράδειγμα που το λύνουμε με τη μέθοδο της λογιστικής παλινδρόμησης και ένα παράδειγμα που το λύνουμε με τη μέθοδο της διαχωριστικής ανάλυσης. Αυτό το κάνουμε με τη βοήθεια του στατιστικού πακέτου SPSS. / In this work we dealt with two methods, that their aim are to classify the observations in known teams and afterwards to make forecasts for new observations. These methods are the accountant regression (logistic regression) and the bisector analysis (discriminant analysis).

Submitted by FREDERICO MONFARDINI null (fred.monf@gmail.com) on 2016-05-04T01:21:27Z No. of bitstreams: 1 DISSERTAÇÃO - FREDERICO.pdf: 1083790 bytes, checksum: e190391e7f59e12ce3b3f062297293e5 (MD5) / Rejected by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: O arquivo submetido está sem a ficha catalográfica. A versão submetida por você é considerada a versão final da dissertação/tese, portanto não poderá ocorrer qualquer alteração em seu conteúdo após a aprovação. Corrija esta informação e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-05-06T14:24:35Z (GMT) / Submitted by FREDERICO MONFARDINI null (fred.monf@gmail.com) on 2016-05-11T01:12:32Z No. of bitstreams: 1 DISSERTAÇÃO - FREDERICO.pdf: 979406 bytes, checksum: 75d1f03b99c1e8e3627b3ee7b3776361 (MD5) / Rejected by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: O mês informado na capa e contra-capa do documento estão diferentes da data de defesa informada na folha de aprovação. Corrija estas informações no arquivo PDF e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-05-13T13:14:09Z (GMT) / Submitted by FREDERICO MONFARDINI null (fred.monf@gmail.com) on 2016-05-16T04:01:38Z No. of bitstreams: 1 DISSSERTAÇÃO - FREDERICO.pdf: 1003174 bytes, checksum: 3449613d0bfa6567b122b1461608bc55 (MD5) / Approved for entry into archive by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br) on 2016-05-16T14:41:59Z (GMT) No. of bitstreams: 1 monfardini_f_me_prud.pdf: 1003174 bytes, checksum: 3449613d0bfa6567b122b1461608bc55 (MD5) / Made available in DSpace on 2016-05-16T14:41:59Z (GMT). No. of bitstreams: 1 monfardini_f_me_prud.pdf: 1003174 bytes, checksum: 3449613d0bfa6567b122b1461608bc55 (MD5) Previous issue date: 2016-02-19 / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Os Modelos Lineares Generalizados (GLM) foram introduzidos no início dos anos 70, tendo um grande impacto no desenvolvimento da teoria estatística. Do ponto de vista teórico, esta classe de modelos representa uma abordagem unificada de muitos modelos estatísticos, correntemente usados nas aplicações, podendo-se utilizar dos mesmos procedimentos de inferência. Com o avanço computacional das últimas décadas foi notável o desenvolvimento de extensões nesta classe de modelos e de métodos para os procedimentos de inferência. No contexto da abordagem Bayesiana, até a década de 80 utilizava-se de métodos aproximados de inferência, tais como aproximação de Laplace, quadratura Gaussiana e outros. No início da década de 90, foram popularizados os métodos de Monte Carlo via Cadeias de Markov (Monte Carlo Markov Chain - MCMC) que revolucionaram as aplicações no contexto Bayesiano. Apesar de serem métodos altamente eficientes, a convergência do algoritmo em modelos complexos pode ser extremamente lenta, o que gera alto custo computacional. Em 2009 surgiu o método de Aproximações de Laplace Aninhadas Integradas (Integrated Nested Laplace Aproximation - INLA) que busca eficiência tanto no custo computacional como na precisão das estimativas. Considerando a importância desta classe de modelos, neste trabalho propõem-se explorar extensões dos MLG para dados longitudinais e recentes propostas apresentadas na literatura para os procedimentos de inferência. Mais especificamente, explorar modelos para dados binários (binomiais) e para dados de contagem (Poisson), considerando a presença de variabilidade extra, incluindo superdispersão e presença de efeitos aleatórios através de modelos hierárquicos e modelos hierárquicos dinâmicos. Além disso, explorar diferentes procedimentos de inferência no contexto Bayesiano, incluindo MCMC e INLA. / Generalized Linear Models (GLM) were introduced in the early 70s, having a great impact on the development of statistical theory. From a theoretical point of view, this class of model is a unified approach to many statistical models commonly used in applications and can be used with the same inference procedures. With advances in the computer over subsequent decades has come a remarkable development of extensions in this class of design and method for inference procedures. In the context of Bayesian approach, until the 80s, it was used to approximate inference methods, such as approximation of Laplace, Gaussian quadrature, etc., The Monte Carlo Markov Chain methods (MCMC) were popularized in the early 90s and have revolutionized applications in a Bayesian context. Although they are highly efficient methods, the convergence of the algorithm in complex models can be extremely slow, which causes high computational cost. The Integrated Nested Laplace Approximations method (INLA), seeking efficiency in both computational cost and accuracy of estimates, appeared in 2009. This work proposes to explore extensions of GLM for longitudinal data considering the importance of this class of model, and recent proposals in the literature for inference procedures. More specifically, it explores models for binary data (binomial) and count data (Poisson), considering the presence of extra variability, including overdispersion and the presence of random effects through hierarchical models and hierarchical dynamic models. It also explores different Bayesian inference procedures in this context, including MCMC and INLA.

Made available in DSpace on 2015-03-26T13:32:20Z (GMT). No. of bitstreams: 1 texto completo.pdf: 475570 bytes, checksum: 32a4377514ec0978d86cb9bc9fcb45f1 (MD5) Previous issue date: 2013-07-31 / A hierarchical Bayesian modeling was used to evaluate the influence of halothane gene and its interaction with sex on pig ́s growth curves. Under this approach, the parameters from growth models (Logistic, Gompertz and von Bertalanffy) were estimated jointly with the effects of halothane gene and sex. A total of 344 F2 (Commercial x Piau) animals were weighted at birth, 21, 42, 63, 77, 105 and 150 days in life. The Logistic model has presented the best fit based on DIC (Deviance Information Criterion). Thus, the samples from marginal posterior distributions for the differences between the parameters estimates of Logistic model have indicated that the maturity weight of males with heterozygous genotypes (HALNn) was superior to males with homozygous genotypes (HALNN). In order to realize a comparison with the traditional methodology, the frequentist approach based on two distinct steps also was used, but there was not identified significant differences between growth curve parameter estimates from each group (combinations of halothane genotypes and sex). / Para avaliar a influência do gene halotano sobre a curva de crescimento de suínos, bem como sua interação com o sexo do animal, foi proposta uma modelagem hierárquica Bayesiana. Nesta abordagem, os parâmetros dos modelos não-lineares de crescimento (Logístico, Gompertz e von Bertalanffy) foram estimados conjuntamente com os efeitos de sexo e genótipos do gene halotano. Foram utilizados 344 animais F2(Comercial x Piau) pesados ao nascer, aos 21, 42, 63, 77, 105 e 150 dias. O modelo Logístico foi aquele que apresentou melhor qualidade de ajuste por apresentar menor DIC (Deviance Information Criterion) que os demais. As amostras das distribuições marginais a posteriori para as diferenças entre as estimativas dos parâmetros do modelo Logístico indicaram que o peso dos machos à idade adulta com genótipo heterozigoto (HALNn) foi superior ao dos homozigotos (HALNN). A título de comparação, também foi considerada a abordagem frequentista tradicional baseada em dois passos distintos, a qual, por apresentar um menor poder de discernimento estatístico, não mostrou diferenças significativas.

A ampliação acelerada da demanda por transporte, mais especificamente pelo transporte rodoviário, tem provocado um aumento expressivo no número de acidentes de trânsito nesse ambiente. Consequentemente, a redução dos acidentes de trânsito tem sido um grande desafio para os pesquisadores e gestores da área rodoviária. Porém, os acidentes de trânsito são eventos complexos se considerados os diversos fatores que podem influenciá-los. Dentro desse contexto esta dissertação apresenta um estudo de modelos de previsão de acidentes, que podem ser utilizados para a avaliação do potencial de segurança em determinados locais, identificação e classificação de localidades perigosas ou com propensão a acidentes e avaliação da eficácia de medidas de melhoria da segurança. Nessa dissertação é apresentado um levantamento teórico e metodológico dos modelos de previsão de acidentes, identificando as principais variáveis adotadas bem como as técnicas utilizadas. Para cada modelo revisado foram verificadas as principais diferenças e limitações, e ainda, a análise das variáveis mais influentes presentes nesses modelos. Após, é feita uma comparação de duas abordagens distintas para estimar modelos de previsão de acidentes. A primeira consiste em estimar a ocorrência de acidentes em segmentos da via com as mudanças de características dos elementos de infraestrutura. O segundo relaciona a frequência de acidentes para um único elemento de infraestrutura da via, chamado na literatura internacional de entidade (ex: interseção, curva, tangente, etc.), com base apenas na variável relacionada ao volume de tráfego. O estudo baseado na comparação dessas duas abordagens para a previsão de acidentes revelou que a utilização do volume de tráfego como única variável independente apresenta resultados semelhantes ou até melhores que os modelos baseados em diversos elementos de infraestrutura da rodovia. / The enlargement and the accelerated development of transportation systems, more specifically the land system, have caused the number of road accidents to increase significantly. Therefore, the reduction of road accidents has been a great challenge for researchers and managers in the field of land transportation. However, considering the various factors that may influence them, road accidents are complex events. In this context, this paper presents a study of accident prediction models that can be used to assess the safety potential in certain locations, identify and rank dangerous locations or areas prone to accidents and evaluate the effectiveness of safety improvement measures. Initially, a theoretical and methodological review of accident prediction models is presented, and both the main variables adopted and the methodologies employed are identified. The main differences between all models reviewed and their limitations are presented, and the most influential variables are analyzed. In a second moment, a comparison of two different accident prediction methods is performed. The first method consists in estimating the occurrence of accidents in road sections with changes in the characteristics of infrastructure elements. The second one relates the frequency of accidents based on a single infrastructure element (intersection, curve, tangent, etc.) based on traffic volume only. The study based on the comparison of these two methods found that the use of traffic volume as the only independent variable yields similar or even better results than the models based on various road infrastructure elements.