کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1149737 957893 2009 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Another look at Huber's estimator: A new minimax estimator in regression with stochastically bounded noise
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
Another look at Huber's estimator: A new minimax estimator in regression with stochastically bounded noise
چکیده انگلیسی

Huber's estimator has had a long lasting impact, particularly on robust statistics. It is well known that under certain conditions, Huber's estimator is asymptotically minimax. A moderate generalization in rederiving Huber's estimator shows that Huber's estimator is not the only choice. We develop an alternative asymptotic minimax estimator and name it regression with stochastically bounded noise (RSBN). Simulations demonstrate that RSBN is slightly better in performance, although it is unclear how to justify such an improvement theoretically. We propose two numerical solutions: an iterative numerical solution, which is extremely easy to implement and is based on the proximal point method; and a solution by applying state-of-the-art nonlinear optimization software packages, e.g., SNOPT. Contribution: the generalization of the variational approach is interesting and should be useful in deriving other asymptotic minimax estimators in other problems.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 139, Issue 2, 1 February 2009, Pages 503–515
نویسندگان
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