Emílio Suyama

Design of economically optimal zero-defect acceptance sampling with rectification when diagnosis errors are present

In this paper we present the optimum sampling size in zero-defect acceptance with rectification sampling scheme in the presence of misclassification errors. Its development is based on an economical model. The procedures are implemented in a program using the software Matlab and illustrated by an example.

Uma abordagem robusta de cálculo aplicada a um sistema potenciométrico de medida de concentrações hidrogeniônicas em soluções aquosas

The data analyzed in this work were generated following the methodology developed by Molina et al.(J. Electroanal. Chem., 1979) for the calibration of a potentiometric system of measurement of hydrogen-ion concentrations resulting from neutralizations, at 25 oC, of acidic or alkaline solutions at constant ionic strength (0.1 mol.l-1) held with NaClO4. The observed data present a serious deviation in relation to the mathematical model derived from the Nernst equation, for pH values ranging from 3 to 11, where pH=-log[H+]. We show that the minimization of the sum of the absolute values of the residuals gives estimates that are not influenced by outlying values.

Redução dos custos de quantificação de benefícios na avaliação contingente

The so-called contingent valuation method is frequently used to quantify the benefits brought about by the implementation of a project with environmental impacts. This method uses data collected in field researches conducted among the potential beneficiaries of the project. The objective of this paper is to show how to use information contained in existing studies for similar projects as a possible way to reduce the costs of the evaluation process. The paper proposes that such information, which supplies preliminary indicators for some crucial parameters, allows to narrow the scope of choices among projects of the same type in new places. The type of project selected for analysis is the construction of a sewage system in several middle-size cities in Brazil.

Inferences in dichotomous classifications with misclassifications based on sequential repetitive classifications

ABSTRACT To create inferences in dichotomous classifications with misclassifications and possibly perform repeated classifications, the maximum likelihood method is commonly used, mainly because of its efficiency in obtaining parameter estimators of a mixture of two binomial distributions. One simpler alternative that is operationally easier is to consider the simple majority method. In this method, each of n items are classified r times as conforming or non-conforming. The final classification of the item is determined by the most frequent class. This method yielded lower mean squared errors than the maximum likelihood and the moments estimators and is asymptotically efficient. In this paper, we introduce a new approach in which the realization of all r repeated classifications of each item may not be needed. Each of n items is sequentially classified as conforming or nonconforming, and the process ceases when the frequency of conforming or non-conforming classification reaches the integer a. We show that, by a Monte Carlo simulation, the last procedure presents a lower mean squared error than the simple majority results for a similar number of r repeated classifications.