On the population median estimation using robust extreme ranked set sampling
-
Amer Ibrahim Al-Omari
and
Amjad D. Al-Nasser
Abstract.
In this paper, the robust extreme ranked set sampling (RERSS) scheme is considered for estimating the population median. The RERSS is compared with the simple random sampling (SRS), ranked set sampling (RSS) and extreme ranked set sampling (ERSS) schemes. A Monte Carlo simulation study is used to study the performance of the median estimator. It is found that RERSS estimators are unbiased of the population median when the underlying distribution is symmetric. Also, in terms of the efficiency criterion; the median estimator based on RERSS is more efficient than the median estimators based on SRS, ERSS, and RSS for symmetric and asymmetric distributions considered in this study. For asymmetric distributions, the RERSS estimators have a smaller bias.
© 2012 by Walter de Gruyter Berlin Boston
Articles in the same Issue
- Masthead
- On the population median estimation using robust extreme ranked set sampling
- A mathematical formalization of the parallel replica dynamics
- A restarted estimation of distribution algorithm for solving sudoku puzzles
- Parallel random number generators in Monte Carlo derivative pricing: An application-based test
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Articles in the same Issue
- Masthead
- On the population median estimation using robust extreme ranked set sampling
- A mathematical formalization of the parallel replica dynamics
- A restarted estimation of distribution algorithm for solving sudoku puzzles
- Parallel random number generators in Monte Carlo derivative pricing: An application-based test
- Probabilistic error bounds for the discrepancy of mixed sequences
