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Master's Dissertation
DOI
10.11606/D.11.2010.tde-24022010-092341
Document
Author
Full name
Guilherme Biz
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2009
Supervisor
Committee
Zocchi, Silvio Sandoval (President)
Leandro, Roseli Aparecida
Sanches, Adhemar
Title in Portuguese
Análise Bayesiana de ensaios fatoriais 2k usando os princípios dos efeitos esparsos, da hierarquia e da hereditariedade
Keywords in Portuguese
Análise de regressão e de correlação
Estatística aplicada
Inferência Bayesiana.
Abstract in Portuguese
No Planejamento de experimentos para o ajuste de modelos polinomiais envolvendo k fatores principais e respectivas interações, e bastante comum a utilização dos fatoriais 2k, 3k ou frações dos mesmos. Para as analises dos resultados desses experimentos, freqüentemente se considera o princípio da hereditariedade, ou seja, uma vez constatada uma interação significativa entre fatores, os fatores que aparecem nesta interação e respectivas interações devem também estar presentes no modelo. Neste trabalho, esse princípio e incorporado diretamente a priori, para um método de seleção de variáveis Bayesiana, seguindo as idéias propostas por Chipman, Hamada e Wu (1997), porem com uma alteração dos valores sugeridos pelos autores para os hiperparâmetros. Essa alteração, proposta neste trabalho, promove uma melhoria considerável na metodologia original. A metodologia e então ilustrada por meio da analise dos resultados de um experimento fatorial para a elaboração de biofilmes de amido originado da ervilha.
Title in English
Bayesian analysis of 2k factorial designs using the sparse eects, hierarchy and heredity principles
Keywords in English
Applied statistics
Bayesian Inference.
Regression analysis and correlation
Abstract in English
In experimental planning for adjustment of polynomials models involving k main factors and their interactions, it is frequent to adopt the 2k, 3k designs or its fractions. Furthermore, it is not unusual, when analysing the results of such experiments, to consider the heredity principle. In other words, once detected a signicant interaction between factors, the factors that appear in this interaction and respective interactions should also be present in the model. In this work, this principle is incorporated directly in the prior, following the ideas proposed by Chipman, Hamada and Wu (1997), but changing some of the hyperparameters. What improves considerably the original methodology. Finally the methodology is illustrated by the analysis of the results of an experiment for the elaboration of pea starch biolms.
 
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Guilherme_Biz.pdf (1.06 Mbytes)
Publishing Date
2010-03-02
 
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