期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 46, 期 9, 页码 6719-6737出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2016.1208229
关键词
Kernel density estimation; Multi-stage ranked set sampling; Nonlinear dependency; Optimal bandwidth selection
资金
- Institute for Research in Fundamental Sciences (IPM), Tehran, Iran [BS-1393-1-02]
This article is concerned with nonparametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estimator in simple random sampling. The applications of the proposed estimator to the mutual information estimation as well as estimation of the Kullback-Leibler divergence are provided. Several Monte-Carlo simulation studies are conducted to examine the performance of the estimator. The results are applied to the longleaf pine (Pinus palustris) trees and the body fat percentage datasets to illustrate applicability of theoretical results.
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