Regiões de confiança para a localização do ponto estacionário em superfícies de resposta, usando o método "bootstrap" Bayesiano / Confidence region on the location of the stationary point in response surfaces, a Bayesian bootstrap approach

AUTOR(ES)
DATA DE PUBLICAÇÃO

2008

RESUMO

Experiments in which one or more response variables are influenced by several quantitative factors are very common in agricultural, chemistry, biology and other areas. In this case, the research question consists in studying this relation, being of great utility the use of response surface methodology (RSM). In this context determining the level of the factors that optimize the response consists finding the coordinates of the stationary point of the model. However, as the true model is unknown, it is of interest to obtain a confidence region of the true coordinates to analyze the precision of the obtained estimate. The procedures for the construction of confidence regions for the coordinates of the stationary point were studied in diferent situations, considering the shape of the surfaces analyzed and the distribution and magnitude of the variance errors. The methodology of Box and Hunter (1954) (BH), bootstrap and Bayesian bootstrap with Mahalanobis distance among the coordinates of the stationary point of the observed sample and those obtained using bootstrap estimates(BM and BBM) and bootstrap and Bayesian bootstrap with non-parametric methods for density estimation (BNP and BBNP) were compared. The methodology evaluation was realized by means of simulation and applied to a peanut yields data set. In simulation study the BH methodology, which is based in normal distribution of errors, presented a good performance in all of the analyzed situations, having concordance among the nominal and real confidence regions, even in those which this distribution is fairly asymmetric. This behavior was also observed in BM and BBM methods. The BNP and BBNP methods did not presented a satisfactory performance, resulting in a real significance level lower than the nominal for the eigenvalue with lower absolute value, generating bigger confidence regions. The inverse was observed using eigenvalue with higher absolute value. In the analysis of the peanut yields data set the BH, BM and BNP methods presented confidence regions larger than the BBM and BBNP methods. The Bayesian bootstrap estimate values are closer of the minimum square estimates and present less dispersion what explain the confidence region lower area.

ASSUNTO(S)

distribuições amostrais bayesian bootstrap metodologia e técnicas de computação respose surface stationary point confidence regions amendoim - estatísticas e dados numéricos inferência bayesiana non parametric density. superfícies de resposta.

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