Bias-Calibrated Estimation from Sample Surveys Containing Outliers
|Published in||Journal of the Royal Statistical Society. B, Statistical Methodology. 1998, vol. 60, no. 2, p. 413-428|
|Abstract||We discuss the problem of estimating finite population parameters on the basis of a sample containing representative outliers. We clarify the motivation for Chamber'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.|
|Keywords||Bias — Bias calibration — Distribution function — Finite population parameters — Model-based estimation — Quantiles — Robust estimation — Totals|
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|WELSH, Alan H., RONCHETTI, Elvezio. Bias-Calibrated Estimation from Sample Surveys Containing Outliers. In: Journal of the Royal Statistical Society. B, Statistical Methodology, 1998, vol. 60, n° 2, p. 413-428. https://archive-ouverte.unige.ch/unige:23224|