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The Performance of a Robust Multistage Estimator in Nonlinear Regression with Heteroscedastic Errors
Stockholm University, Faculty of Social Sciences, Department of Statistics. Islamic Azad University, Iran.
2016 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 45, no 9, 3394-3415 p.Article, review/survey (Refereed) Published
Abstract [en]

In this article, a robust multistage parameter estimator is proposed for nonlinear regression with heteroscedastic variance, where the residual variances are considered as a general parametric function of predictors. The motivation is based on considering the chi-square distribution for the calculated sample variance of the data. It is shown that outliers that are influential in nonlinear regression parameter estimates are not necessarily influential in calculating the sample variance. This matter  persuades us, not only to robustify the estimate of the parameters of the models for both the regression function and the variance, but also to replace the sample variance of the data by a robust scale estimate.

Place, publisher, year, edition, pages
2016. Vol. 45, no 9, 3394-3415 p.
Keyword [en]
Nonlinear regression, outliers, heteroscedastic errors, sample variances, robust statistics
National Category
Probability Theory and Statistics
Research subject
URN: urn:nbn:se:su:diva-106623DOI: 10.1080/03610918.2014.944657ISI: 000382518900020OAI: diva2:737417
Available from: 2014-08-12 Created: 2014-08-12 Last updated: 2016-10-03Bibliographically approved

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