A new biparametric survival model: classical and Bayesian inference
New mixed binomial regression models to unbalancing data and extensions
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Author(s): |
Camila Borelli Zeller
Total Authors: 1
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Document type: | Master's Dissertation |
Press: | Campinas, SP. |
Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica |
Defense date: | 2006-02-23 |
Examining board members: |
Filidor Edilfonso Vilca Labra;
Elisete da Conceição Quintaneiro Aubin;
Heleno Bolfarine
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Advisor: | Filidor Edilfonso Vilca Labra |
Abstract | |
In this work, we presented a study of statistical inference in the Grubbs's model with subgroups, that represents an extension of the model proposed by Grubbs (1948,1973) that is frequently used to compare instruments or measurement methods. We considered the parametrization proposed by Bedrick (2001). The study is based on the maximum likelihood method. Tests of hypotheses are considered and based on the wald statistics, score and likelihood ratio statistics. The maximum likelihood estimators of the Grubbs's model with subgroups are obtained using the algorithm EM and considering that the observations follow a normal distribution. We also presented a study of diagnostic analysis in the Grubb's model with subgroups with the interest of evaluating the effect that a certain one subgroup exercises in the estimate of the parameters. We will use the methodology of local influence proposed by Cook (1986) considering the schemes of perturbation of case weights. Finally, we presented some simulation studies and we illustrated the obtained theoretical results using data found in the literature (AU) |