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[en] ANALYSIS OF MEDIA E DISPERSION IN UNREPLICATED FACTORIAL EXPERIMENTS FOR THE OPTIMIZATION OF INDUSTRIAL PROCESSES / [pt] ANÁLISE DA MÉDIA E DISPERSÃO EM EXPERIMENTOS FATORIAIS NÃO REPLICADOS PARA OTIMIZAÇÃO DE PROCESSOS INDUSTRIAISANTONIO FERNANDO DE CASTRO VIEIRA 20 December 2004 (has links)
[pt] Esta tese reúne as técnicas estatísticas indicadas para a
modelagem da média e
da dispersão das características de qualidade de processos
e produtos, em
experimentos fatoriais não replicados, resultando na
definição de um roteiro
integrado e detalhado de análise. A motivação vem de que,
apesar de haver várias
publicações sobre regressão linear clássica, modelos
lineares generalizados (MLG),
transformação da resposta e planejamento de experimentos,
não existe um texto que
reúna e descreva em detalhe todos os aspectos da modelagem
da média e da
dispersão em experimentos fatoriais. Os poucos textos sobre
esse assunto não
descrevem vários aspectos importantes em estudos dessa
natureza, por exemplo,
como são aplicados os testes de significância dos
coeficientes dos MLG, e quais são
as estatísticas e os gráficos indicados para verificar a
adequação do modelo.
Ademais, nada foi encontrado na literatura sobre a
identificação de modelos em
experimentos fatoriais. Todos esses aspectos são detalhados
nessa tese. Uma vez
construído o modelo, é mostrado como usá-lo para obter as
condições ótimas de
operação dos processos e produtos. Além do cumprimento
desse objetivo principal,
a tese traz algumas contribuições adicionais; a saber: a)
aponta limitações em todos
quatro métodos da literatura que se propõem a escolher a
transformação mais
adequada para a resposta. Esses métodos não produziram
resultados satisfatórios
quando houve interações significativas entre os fatores; b)
propõe a utilização de
métodos de transformação da resposta como fonte de
indicação da função de ligação
a ser usada nos modelos lineares generalizados; e c) propõe
a utilização da função
de log-verossimilhança para uma escolha conjunta da
distribuição de probabilidade
e da função de ligação, nos modelos lineares generalizados. / [en] This thesis puts together the statistical techniques
indicated for modelling the
mean and dispersion of quality characteristics of products
and processes via
unreplicated factorial experiments, resulting in the
definition of an integrated and
detailed script for the analysis. It was motivated by the
fact that, although there are
many publications about classic linear regression,
generalized linear models
(GLMs), response transformation and design of experiments,
there is no one text
which put together and describe in detail all the aspects
of the modelling of the
mean and the dispersion in factorial experiments. The few
texts on the subject do
not describe a number of important aspects in studies of
this nature, e.g. how
significance tests for the coefficients in GLMs should be
applied and which are the
statistics and plots indicated for checking model adequacy.
In addition, nothing
was found in the literature about model identification in
factorial experiments. All
these aspects are detailed in this thesis. Once the model
is built, we show how to
use it in order to obtain the optimal operating conditions
for products and
processes. Besides achieving this main objective, the
thesis brings some additional
contributions, namely: a) it points out limitations in all
the four methods in the
literature which have the purpose of selecting the most
adequate transformation of
the response; b) it proposes using response transformation
methods as a source of
indication of the link function to use in GLMs, and c) it
proposes using the loglikelihood
function for the joint choice of the probability
distribution and of the
link function in GLMs.
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Modelos lineares generalizados mistos multivariados para caracterização genética de doenças / Multivariate generalized linear mixed models for genetic characterization of diseasesBaldoni, Pedro Luiz, 1989- 24 August 2018 (has links)
Orientador: Hildete Prisco Pinheiro / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação / Made available in DSpace on 2018-08-24T09:34:36Z (GMT). No. of bitstreams: 1
Baldoni_PedroLuiz_M.pdf: 4328843 bytes, checksum: 0ab04f375988e62ac31097716ac0eaa5 (MD5)
Previous issue date: 2014 / Resumo: Os Modelos Lineares Generalizados Mistos (MLGM) são uma generalização natural dos Modelos Lineares Mistos (MLM) e dos Modelos Lineares Generalizados (MLG). A classe dos MLGM estende a suposição de normalidade dos dados permitindo o uso de várias outras distribuições bem como acomoda a superdispersão frequentemente observada e também a correlação existente entre observações em estudos longitudiais ou com medidas repetidas. Entretanto, a teoria de verossimilhança para MLGM não é imediata uma vez que a função de verossimilhança marginal não possui forma fechada e envolve integrais de alta dimensão. Para solucionar este problema, diversas metodologias foram propostas na literatura, desde técnicas clássicas como quadraturas numéricas, por exemplo, até métodos sofisticados envolvendo algoritmo EM, métodos MCMC e quase-verossimilhança penalizada. Tais metodologias possuem vantagens e desvantagens que devem ser avaliadas em cada tipo de problema. Neste trabalho, o método de quase-verossimilhança penalizada (\cite{breslow1993approximate}) foi utilizado para modelar dados de ocorrência de doença em uma população de vacas leiteiras pois demonstrou ser robusto aos problemas encontrados na teoria de verossimilhança deste conjunto de dados. Além disto, os demais métodos não se mostram calculáveis frente à complexidade dos problemas existentes em genética quantitativa. Adicionalmente, estudos de simulação são apresentados para verificar a robustez de tal metodologia. A estabilidade dos estimadores e a teoria de robustez para este problema não estão completamente desenvolvidos na literatura / Abstract: Generalized Linear Mixed Models (GLMM) are a generalization of Linear Mixed Models (LMM) and of Generalized Linear Models (GLM). The class of models GLMM extends the normality assumption of the data and allows the use of several other probability distributions, for example, accommodating the over dispersion often observed and also the correlation among observations in longitudinal or repeated measures studies. However, the likelihood theory of the GLMM class is not straightforward since its likelihood function has not closed form and involves a high order dimensional integral. In order to solve this problem, several methodologies were proposed in the literature, from classical techniques as numerical quadrature¿s, for example, up to sophisticated methods involving EM algorithm, MCMC methods and penalized quasi-likelihood. These methods have advantages and disadvantages that must be evaluated in each problem. In this work, the penalized quasi-likelihood method (\cite{breslow1993approximate}) was used to model infection data in a population of dairy cattle because demonstrated to be robust in the problems faced in the likelihood theory of this data. Moreover, the other methods do not show to be treatable faced to the complexity existing in quantitative genetics. Additionally, simulation studies are presented in order to verify the robustness of this methodology. The stability of these estimators and the robust theory of this problem are not completely studied in the literature / Mestrado / Estatistica / Mestre em Estatística
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Locally Optimal Experimental Designs for Mixed Responses ModelsJanuary 2020 (has links)
abstract: Bivariate responses that comprise mixtures of binary and continuous variables are common in medical, engineering, and other scientific fields. There exist many works concerning the analysis of such mixed data. However, the research on optimal designs for this type of experiments is still scarce. The joint mixed responses model that is considered here involves a mixture of ordinary linear models for the continuous response and a generalized linear model for the binary response. Using the complete class approach, tighter upper bounds on the number of support points required for finding locally optimal designs are derived for the mixed responses models studied in this work.
In the first part of this dissertation, a theoretical result was developed to facilitate the search of locally symmetric optimal designs for mixed responses models with one continuous covariate. Then, the study was extended to mixed responses models that include group effects. Two types of mixed responses models with group effects were investigated. The first type includes models having no common parameters across subject group, and the second type of models allows some common parameters (e.g., a common slope) across groups. In addition to complete class results, an efficient algorithm (PSO-FM) was proposed to search for the A- and D-optimal designs. Finally, the first-order mixed responses model is extended to a type of a quadratic mixed responses model with a quadratic polynomial predictor placed in its linear model. / Dissertation/Thesis / Doctoral Dissertation Statistics 2020
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Modely s Touchardovm rozdÄlenm / Models with Touchard DistributionIbukun, Michael Abimbola January 2021 (has links)
In 2018, Raul Matsushita, Donald Pianto, Bernardo B. De Andrade, Andre Can§ado & Sergio Da Silva published a paper titled âTouchard distributionâ, which presented a model that is a two-parameter extension of the Poisson distribution. This model has its normalizing constant related to the Touchard polynomials, hence the name of this model. This diploma thesis is concerned with the properties of the Touchard distribution for which delta is known. Two asymptotic tests based on two different statistics were carried out for comparison in a Touchard model with two independent samples, supported by simulations in R.
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The Application of Mean-Variance Relationships to General Recognition TheoryWoodbury, George 28 September 2021 (has links)
No description available.
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On The Jackknife Averaging of Generalized Linear ModelsZulj, Valentin January 2020 (has links)
Frequentist model averaging has started to grow in popularity, and it is considered a good alternative to model selection. It has recently been applied favourably to gen- eralized linear models, where it has mainly been purposed to aid the prediction of probabilities. The performance of averaging estimators has largely been compared to that of models selected using AIC or BIC, without much discussion of model screening. In this paper, we study the performance of model averaging in classification problems, and evaluate performances with reference to a single prediction model tuned using cross-validation. We discuss the concept of model screening and suggest two methods of constructing a candidate model set; averaging over the models that make up the LASSO regularization path, and the so called LASSO-GLM hybrid. By means of a Monte Carlo simulation study, we conclude that model averaging does not necessarily offer any improvement in classification rates. In terms of risk, however, we see that both methods of model screening are efficient, and their errors are more stable than those achieved by the cross-validated model of comparison.
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Predicting customer level risk patterns in non-life insurance / Prediktering av riskmönster på kundnivå i sakförsäkringVillaume, Erik January 2012 (has links)
Several models for predicting future customer profitability early into customer life-cycles in the property and casualty business are constructed and studied. The objective is to model risk at a customer level with input data available early into a private consumer’s lifespan. Two retained models, one using Generalized Linear Model another using a multilayer perceptron, a special form of Artificial Neural Network are evaluated using actual data. Numerical results show that differentiation on estimated future risk is most effective for customers with highest claim frequencies.
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Surviving the Surge: Real-time Analytics in the Emergency DepartmentRea, David J. 05 October 2021 (has links)
No description available.
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Feature Screening for High-Dimensional Variable Selection In Generalized Linear ModelsJiang, Jinzhu 02 September 2021 (has links)
No description available.
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Using an Experimental Mixture Design to Identify Experimental Regions with High Probability of Creating a Homogeneous Monolithic Column Capable of FlowWillden, Charles C. 16 April 2012 (has links) (PDF)
Graduate students in the Brigham Young University Chemistry Department are working to develop a filtering device that can be used to separate substances into their constituent parts. The device consists of a monomer and water mixture that is polymerized into a monolith inside of a capillary. The ideal monolith is completely solid with interconnected pores that are small enough to cause the constituent parts to pass through the capillary at different rates, effectively separating the substance. Although the end objective is to minimize pore sizes, it is necessary to first identify an experimental region where any combination of input variables will consistently yield homogeneous monoliths capable of flow. To accomplish this task, an experimental mixture design is used to model the relationship between the variables related to the creation of the monolith and the probability of creating an acceptable polymer. The results of the mixture design suggest that, inside of the constrained experimental region, mixtures with higher proportions of monomer and surfactant, low amounts of initiator and salt, and DEGDA as the monomer have the highest probability of producing a workable monolith. Confirmatory experiments are needed before future experimentation to minimize pore sizes is performed using the refined constrained experimental region determined by the results of this analysis.
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