Bias‐calibrated estimation from sample surveys containing outliers
1998
Welsh, A. H. | Ronchetti, Elvezio
We discuss the problem of estimating finite population parameters on the basis of a sample containing representative outliers. We clarify the motivation for Chambers's bias‐calibrated estimator of the population total and show that bias calibration is a key idea in constructing estimators of finite population parameters. We then link the problem of estimating the population total to distribution function or quantile estimation and explore a methodology based on the use of Chambers's estimator. We also propose methodology based on the use of robust estimates and a bias‐calibrated form of the Chambers and Dunstan estimator of the population distribution function. This proposal leads to a bias‐calibrated estimator of the population total which is an alternative to that of Chambers. We present a small simulation study to illustrate the utility of these estimators.
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