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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Modeling Diseases With Multiple Disease Characteristics: Comparison Of Models And Estimation Methods

Erdem, Munire Tugba 01 July 2011 (has links) (PDF)
Epidemiological data with disease characteristic information can be modelled in several ways. One way is taking each disease characteristic as a response and constructing binary or polytomous logistic regression model. Second way is using a new response which consists of disease subtypes created by cross-classification of disease characteristic levels, and then constructing polytomous logistic regression model. The former may be disadvantageous since any possible covariation between disease characteristics is neglected, whereas the latter can capture that covariation behaviour. However, cross-classifying the characteristic levels increases the number of categories of response, so that dimensionality problem in parameter space may occur in classical polytomous logistic regression model. A two staged polytomous logistic regression model overcomes that dimensionality problem. In this thesis, study is progressen in two main directions: simulation study and data analysis parts. In simulation study, models that capture the covariation behaviour are compared in terms of the response model parameter estimators. That is, performances of the maximum likelihood estimation (MLE) approach to classical polytomous logistic regression, Bayesian estimation approach to classical polytomous logistic regression and pseudo-conditional likelihood (PCL) estimation approach to two stage polytomous logistic regression are compared in terms of bias and variation of estimators. Results of the simulation study revealed that for small sized sample and small number of disease subtypes, PCL outperforms in terms of bias and variance. For medium scaled size of total disease subtypes situation when sample size is small, PCL performs better than MLE, however when the sample size gets larger MLE has better performance in terms of standard errors of estimates. In addition, sampling variance of PCL estimators of two stage model converges to asymptotic variance faster than the ML estimators of classical polytomous logistic regression model. In data analysis, etiologic heterogeneity in breast cancer subtypes of Turkish female cancer patients is investigated, and the superiority of the two stage polytomous logistic regression model over the classical polytomous logistic model with disease subtypes is represented in terms of the interpretation of parameters and convenience in hypothesis testing.
2

Aplicação de componentes principais e regressões logísticas múltiplas em sistema de informações geográficas para a predição e o mapeamento digital de solos / Application of principal components and multiple logistic regression in a geographical information system for prediction and digital soil mapping

Caten, Alexandre Ten 31 October 2008 (has links)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Social demands on soil information have grown dramatically, meanwhile the soil surveys are seldom carried out in the country. Digital soil mapping techniques can be applied to infer the spatial distribution of soil from existing soil maps or from reference areas, extrapolating this information to areas not mapped. The purpose of this study was to apply in a Geographic Information System the Multiple Logistic Regressions (MLR) using Principal Components (PC) as explanatory variables to predict soil classes spatial distribution. The study area was the region of municipality São Pedro do Sul / RS. For the development of predictive models a set of nine terrain attributes were used. Model training was executed on an existing soil map and with a survey carried out in a reference area, both in a 1:50.000 scale. The first three retained PC explained 65.57% of the data variability. The predictive models which used PC had lower values of kappa index. The most accurate predicted map reached a kappa value of 63.20% and was generated by using the nine attributes of land as predictive covariates. The mapping accuracy is sensitive to similarities between the mapped classes, and mapping in a more homogeneous categorical level reduces the accuracy of the predicted maps. Soil classes relatively not representative in the training maps are not properly spatialized. The use of MLR allows spatializing of soil classes to areas not mapped, although the use of PC needs to be tested with a larger number of covariates. / As demandas da sociedade pela informação solo têm crescido, porém levantamentos pedológicos praticamente não ocorrem mais no país. Técnicas de Mapeamento Digital do Solo podem ser empregadas para inferir a distribuição espacial de classes de solos a partir de mapas existentes e áreas de referência, extrapolando esta informação para áreas não mapeadas. O objetivo deste estudo foi empregar em um Sistema de Informações Geográficas as Regressões Logísticas Múltiplas (RLM) utilizando-se de Componentes Principais (CP) como variáveis explicativas para a predição espacial de classes de solos. A área de estudo foi na região do município de São Pedro do Sul / RS. Para o desenvolvimento dos modelos preditivos foram utilizados um conjunto de nove atributos do terreno. O treinamento dos modelos foi executado em um mapa de solos existente, e em um levantamento realizado em áreas de referência, ambos na escala 1:50.000. As três primeiras CP retidas explicaram 65,57% da variabilidade dos dados. Os modelos preditivos que empregaram CP obtiveram menores valores do índice kappa. O mapa predito mais acurado empregou os nove atributos do terreno e alcançou um valor de kappa de 63,20%. A acurácia do mapeamento é sensível a semelhança entre as classes mapeadas, e o mapeamento em níveis categóricos mais homogêneos reduz a precisão dos mapas preditos. Classes de solos relativamente pouco representativas não são corretamente espacializadas. O emprego de RLM permite espacializar classes de solos para áreas não mapeadas, embora o emprego de CP necessite ser testado com um maior número de covariáveis.

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