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Master's Dissertation
DOI
https://doi.org/10.11606/D.55.2006.tde-02082006-153701
Document
Author
Full name
Daniel Fernando de Favari
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2006
Supervisor
Committee
Pinto Junior, Dorival Leão (President)
Andrade Filho, Mário de Castro
Demetrio, Clarice Garcia Borges
Title in Portuguese
"Uma aplicação industrial de regressão binária com erros na variável explicativa"
Keywords in Portuguese
algoritmo EM
erros de medição
método analítico
método Delta
regressão binária
teorema de Fieller
Abstract in Portuguese
Neste trabalho, aplicamos um modelo de regressão binária com erros de medição na variável explicativa para analisar sistemas de medição do tipo atributo. Para isto, utilizamos o modelo logístico com erros na variável, para o qual obtemos as estimativas de máxima verossimilhança via o algoritmo EM e a matriz de informação de Fisher observada. Além disso, fizemos um estudo de simulação para compararmos o método analítico e os modelos logístico sem erros na variável (ingênuo) e logístico com erros na variável. Finalmente, aplicamos nossa metodologia para avaliarmos um sistema de medição passa/não passa da maior montadora de motores Diesel (MWM International).
Title in English
"An industrial application of binary regression with errors-in-variable explanatory"
Keywords in English
analytic method
binary regression
Delta method
EM algorithm
Fieller's theorem
measurement errors-in-variable
Abstract in English
In this work, we apply a study of binary regression model with errors-in-variable to analyze attributive measurement systems. For this, we use the logistic model with errors-in-variable to obtain parameter estimates of maximum likelihood through EM algorithm and the observed Fisher information matrix. In addition we do a simulation study to compare analytic method and the logistic model with and without measurement errors-in-variable. Finally, we apply our methodology to evaluate a attributive measurement system for the largest Diesel motor company of the world (MWM International).
 
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Publishing Date
2006-08-25
 
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