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Corrected maximum-likelihood estimation in a class of symmetric nonlinear regression models

✍ Scribed by Gauss M. Cordeiro; Silvia L.P. Ferrari; Miguel A. Uribe-Opazo; Klaus L.P. Vasconcellos

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
122 KB
Volume
46
Category
Article
ISSN
0167-7152

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✦ Synopsis

In this paper we derive general formulae for second-order biases of maximum-likelihood estimates in a class of symmetric nonlinear regression models. This class of models is commonly used for the analysis of data containing extreme or outlying observations in samples from a supposedly normal distribution. The formulae of the biases can be computed by means of an ordinary linear regression. They generalize some previous results by Cook et al.

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