Robust Linear Calibration
作者:
Christos P. Kitsos,
christine H. Müller,
期刊:
Statistics
(Taylor Available online 1995)
卷期:
Volume 27,
issue 1-2
页码: 93-106
ISSN:0233-1888
年代: 1995
DOI:10.1080/02331889508802513
出版商: Gordon & Breach Science Publishers
关键词: AMS 1991 subject classifications;62J05;62F35;62G20;62K05;Linear calibration;classical estimator;conditional contamination;robust estimation;one-step-M-estimator;local optimality;optimal design;asymptotic efficiency;maximin efficiency
数据来源: Taylor
摘要:
We regard the simple linear calibration problem where only the responseyof the regression liney= β0+ β1tis observed with errors. The experimental conditionstare observed without error. For the errors of the observationsywe assume that there may be some gross errors providing outlying observations. This situation can be modeled by a conditionally contaminated regression model. In this model the classical calibration estimator based on the least squares estimator has an unbounded asymptotic bias. Therefore we introduce calibration estimators based on robust one-step-M-estimators which have a bounded asymptotic bias. For this class of estimators we discuss two problems: The optimal estimators and their corresponding optimal designs. We derive the locally optimal solutions and show that the maximin efficient designs for non-robust estimation and robust estimation coincide.
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