In this paper we propose two bias correction approaches in order to reduce the prediction bias of the robust M-quantile predictors in small area estimation in the presence of representative outliers. A Monte-Carlo simulation study is conducted. Results confirm that our approaches improve the efficiency and reduce the prediction bias of M-quantile predictors when the population contains units that may be influential if selected in the sample.
Controlling the bias for M-quantile estimators for small areas
Gaia Bertarelli;
2021-01-01
Abstract
In this paper we propose two bias correction approaches in order to reduce the prediction bias of the robust M-quantile predictors in small area estimation in the presence of representative outliers. A Monte-Carlo simulation study is conducted. Results confirm that our approaches improve the efficiency and reduce the prediction bias of M-quantile predictors when the population contains units that may be influential if selected in the sample.File in questo prodotto:
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