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Scientific article
English

Bias-Calibrated Estimation from Sample Surveys Containing Outliers

Publication date1998
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
Citation (ISO format)
WELSH, Alan H., RONCHETTI, Elvezio. Bias-Calibrated Estimation from Sample Surveys Containing Outliers. In: Journal of the Royal Statistical Society. Series B, Statistical methodology, 1998, vol. 60, n° 2, p. 413–428. doi: 10.1111/1467-9868.00133
Identifiers
ISSN of the journal1369-7412
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