Reduction of optimal calibration dimension with a new optimal auxiliary vector for calibrated estimators of the distribution function
Metadatos
Mostrar el registro completo del ítemMateria
Survey sampling Distribution function Auxiliary information
Fecha
2022Referencia bibliográfica
- Martínez, S., Rueda, M. D. M., & Illescas, M. D. (2022). Reduction of optimal calibration dimension with a new optimal auxiliary vector for calibrated estimators of the distribution function. Mathematical Methods in the Applied Sciences, 45(17), 10959-10981.
Resumen
The calibration method has been widely used to incorporate auxiliary information
in the estimation of various parameters. Specifically, adapted this method to estimate
the distribution function, although their proposal is computationally simple,
its efficiency depends on the selection of an auxiliary vector of points. This work
deals with the problem of selecting the calibration auxiliary vector that minimize the
asymptotic variance of the calibration estimator of distribution function. The optimal
dimension of the optimal auxiliary vector is reduced considerably with respect
to previous studies so that with a smaller set of points the minimum of the asymptotic
variance can be reached, which in turn allows to improve the efficiency of the
estimates.