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Robustifying the Least Squares estimate of parameters of variance model function in nonlinear regression with heteroscedastic variance
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2012 (English)Conference paper, Poster (Refereed)
Abstract [en]

The purpose of this research is to propose a robust estimate for the parameters of a nonlinear regression model and its residual variance model parameters, when the residuals follow a heteroscedastic parametric model function. The classic estimate is based on the least squares estimation error for the model parameters and the least square estimate error between sample variance and variance model, for the parameters of variance function model. The sample variance that are computed from the data set, are used as the initial estimates of variance model. In the presence of outliers these estimators are not Robust, and tends to infinity. Both function model parameter estimates and variance model parameter estimates must be robustified to solve the outlier effect problems. In this research the MM-estimator is applied for robust estimating the function model parameters and M-estimator is applied for robust estimating of variance function model parameters. These estimators  finally combined and the Extended Generalized Estimator is calculated. 

Place, publisher, year, edition, pages
Telford UK, 2012.
Keyword [en]
Robust Statistics, Nonlinear Regression, Heteroscedastic variance
National Category
Probability Theory and Statistics
Research subject
URN: urn:nbn:se:su:diva-85910OAI: diva2:585712
Royal Statistical Society 2012 International Conference: 3-6 September, Telford UK
Robustifying the Least square estimate of parameter of variance model functions in nonlinear regression with heterogeneous variance, Islamic Azad Lamerd university
Available from: 2013-01-17 Created: 2013-01-10 Last updated: 2014-11-18

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Riazoshams, Hossein
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ReferencesLink to record
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