On Kernel-based Estimation of Distribution Function and Its Quantiles Based on Ranked Set Sampling

Document Type

Article

Publication Date

12-12-2022

Publication Title

Journal of Statistical Computation and Simulation

DOI

10.1080/00949655.2022.2153252

Abstract

This paper proposes to use the convex combination of several kernel estimators based on the ranked set sampling (RSS) scheme for estimating the underlying distribution function to construct more efficient quantiles estimation. Several different estimators of quantile function based on the simple random sampling and ranked set sampling are provided. The variance and mean-squared error of the proposed estimators are derived as explicit forms. Theoretical derivation and an intensive simulation study are used to compare the performance of the proposed method with other methods in the literature. The effect of imperfect rankings on the performance of the proposed estimators is investigated. Real data example from the China Health and Nutrition Survey is used to illustrate our proposed approach. Some discussions using another two real data are presented. It observes that the proposed estimators have different behaviours with respect to symmetric and asymmetric distributions.

Comments

Georgia Southern University faculty member, Hani Samawi co-authored On Kernel-based Estimation of Distribution Function and Its Quantiles Based on Ranked Set Sampling.

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