کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1149442 | 957879 | 2010 | 20 صفحه PDF | دانلود رایگان |
Robust statistics allows the distribution of the observations to be any member of a suitable neighborhood about an ideal model distribution. In this paper, the ideal models are semiparametric with finite-dimensional parameter of interest and a possibly infinite-dimensional nuisance parameter.In the asymptotic setup of shrinking neighborhoods, we derive and study the Hampel-type problem and the minmax MSE-problem. We show that, for all common types of neighborhood systems, the optimal influence function ψ˜ can be approximated by the optimal influence functions ψ˜n for certain parametric models.For general semiparametric regression models, we determine (ψ˜n)n∈N in case of error-in-variables and in case of error-free-variables.Finally, the results are applied to Cox regression where we compare our approach to that of Bednarski [1993. Robust estimation in Cox's regression model. Scand. J. Statist. 20, 213–225] in a small simulation study and on a real data set.
Journal: Journal of Statistical Planning and Inference - Volume 140, Issue 1, 1 January 2010, Pages 226–245