کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
695379 1460657 2015 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
On the end-performance metric estimator selection
ترجمه فارسی عنوان
در انتخاب برآوردگر متریک نهایی عملکرد
کلمات کلیدی
انتخاب تقصیر، متریک نهایی عملکرد، برآورد کردن، حداکثر احتمال، کمترین مربعات
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی کنترل و سیستم های مهندسی
چکیده انگلیسی

It is well known that appropriately biasing an estimator can potentially lead to a lower mean square error (MSE) than the achievable MSE within the class of unbiased estimators. Nevertheless, the choice of an appropriate bias is generally unclear and only recently there have been attempts to systematize such a selection. These systematic approaches aim at introducing MSE bounds that are lower than the unbiased Cramér–Rao bound (CRB) for all values of the unknown parameters and at choosing biased estimators that beat the standard maximum-likelihood (ML) and/or least squares (LS) estimators in the finite sample case. In this paper, we take these approaches one step further and investigate the same problem from the aspect of an end-performance metric different than the classical MSE. This study is motivated by recent advances in the area of system identification indicating that the optimal experiment design should be done by taking into account the end-performance metric of interest and not by quantifying a quadratic distance of the unknown model from the true one.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 58, August 2015, Pages 22–27
نویسندگان
, , , , ,