Geodesic
Asymptotic properties of one-step weighted $M$-estimators with application to some regression problems.
Teoriâ veroâtnostej i ee primeneniâ, Tome 62 (2017) no. 3, pp. 468-498 Cet article a éte moissonné depuis la source Math-Net.Ru

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We study the asymptotic behavior of one-step weighted $M$-estimators based on independent not necessarily identically distributed observations, which approximate consistent weighted $M$-estimators. We find sufficient conditions for asymptotic normality of these estimators. As an application, we consider some known regression models where the one-step estimation under consideration allows us to construct explicit asymptotically optimal estimators having the same accuracy as the least-squares or quasi-likelihood estimators.
Keywords: one-step $M$-estimators, one-step weighted $M$-estimators, $M$-estimators, asymptotic normality, Newton's iteration method, initial estimator, nonlinear regression. 
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Yu. Yu. Linke. Asymptotic properties of one-step weighted $M$-estimators with application to some regression problems.. Teoriâ veroâtnostej i ee primeneniâ, Tome 62 (2017) no. 3, pp. 468-498. http://geodesic.mathdoc.fr/item/TVP_2017_62_3_a2/

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