The optimization problem of quantile and poverty measures estimation based on calibration
Identificadores
URI: http://hdl.handle.net/10835/14851
DOI: https://doi.org/10.1016/j.cam.2020.113054
DOI: https://doi.org/10.1016/j.cam.2020.113054
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2020-06-12Resumen
New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish ...
Palabra/s clave
Optimization
calibration technique
poverty measure estimates
survey sampling