Authors: Gabriela Ciuperca
Publish Date: 2009/07/28
Volume: 63, Issue: 4, Pages: 717-743
Abstract
The paper considers the least absolute deviations estimator in a nonlinear parametric regression The interest of the LAD method is its robustness with respect to other traditional methods when the errors of model contain outliers First in the absence of changepoints the convergence rate of estimated parameters is found For a model with changepoints in the case when the number of jumps is known the convergence rate and the asymptotic distribution of estimators are obtained Particularly it is shown that the changepoints estimator converges weakly to the minimizer of given random process Next when the number of jumps is unknown its consistent estimator is proposed via the modified Schwarz criterion
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