Regression Estimator Using Double Ranked Set Sampling

Hani Samawi, Eman M. Tawalbeh

Research output: Contribution to journalArticlepeer-review

Abstract

The performance of a regression estimator based on the double ranked set sample (DRSS) scheme, introduced by Al-Saleh and Al-Kadiri (2000), is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS) or ranked set sampling (RSS) (Yu and Lam, 1997) regression estimator.  Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4) and DRSS for high correlation coefficient (at least 0.91.) The theory is illustrated using a real data set of trees.
Original languageAmerican English
JournalSultan Qaboos University Journal for Science
Volume7
DOIs
StatePublished - 2002

Keywords

  • Double extreme ranked set sample
  • Regression estimator

DC Disciplines

  • Biostatistics

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