Estimation of Distribution Function Based on Ranked Set Sampling: Missing Data Approach

Authors

  • Samir Kamel Ashour Mathematical Statistics, Institute of Statistical Studies and Research, Cairo University, Giza, Egypt
  • Mohamed Soliman Abdallah Quantitative Techniques, Faculty of Commerce, Aswan University, Aswan, Egypt

Keywords:

Cumulative distribution function, concomitant variable, EM algorithm, ranked set sample

Abstract

In many studies, it is a well-established fact that estimation under either ranked set sample (RSS) or its variations is much better than other popular sampling techniques. In this article, missing data approach is adopted to present new estimators for distribution function under RSS setup. Using EM algorithm and linear interpolation technique, new cumulative distribution function (CDF) estimators are proposed. The consistency of the new estimators is analytically discussed. It merges from Monte Carlo simulations that the proposed estimators have a good performance as compared with their competitors based on simulated as well as empirical data set.

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Published

2019-12-12

How to Cite

Ashour, S. K., & Abdallah, M. S. (2019). Estimation of Distribution Function Based on Ranked Set Sampling: Missing Data Approach. Thailand Statistician, 18(1), 27–42. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/228880

Issue

Vol. 18 No. 1 (2020): January

Section

Articles