A RANKED SET SAMPLING MODIFIED RATIO ESTIMATOR

Even order ranked set sampling (EORSS) is a novel proposed ranked set sampling scheme connected with an auxiliary variable correlated with the study variable. This scheme quantifies only the one sampling unit which is at even position from each ranking set by employing specific criteria. The performance of the ratio estimator under EORSS is compared to its contemporary estimators in simple random sampling (SRS), ranked set sampling (RSS), median ranked set sampling (MRSS) and quartile ranked set sampling (QRSS) exploiting the same number of quantified units. The simulation results proved that EORSS is an efficient alternative sampling scheme for ratio estimation than SRS, RSS, MRSS and QRSS.

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Ratio estimator for the population mean using ranked set sampling

Statistical Papers ◽

10.1007/s00362-007-0079-y ◽

2007 ◽

Vol 50 (2) ◽

pp. 301-309 ◽

Cited By ~ 32

Author(s):

Cem Kadilar ◽

Yesim Unyazici ◽

Hulya Cingi

Keyword(s):

Ranked Set Sampling ◽

Ratio Estimator ◽

Population Mean

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Generalized Chain Regression-cum-Chain Ratio Estimator for Population Mean under Stratified Extreme-cum-Median Ranked Set Sampling

Mathematical Problems in Engineering ◽

10.1155/2022/9556587 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Asad Ali ◽

Muhammad Moeen Butt ◽

Muhammad Zubair

Keyword(s):

Real Life ◽

Auxiliary Information ◽

Ranked Set Sampling ◽

Efficient Estimation ◽

Ratio Estimator ◽

Auxiliary Variables ◽

Study Variable ◽

Data Set ◽

Population Mean ◽

Median Ranked Set Sampling

Estimation of population mean of study variable Y suffers loss of precision in the presence of high variation in the data set. The use of auxiliary information incorporated in construction of an estimator under ranked set sampling scheme results in efficient estimation of population mean. In this paper, we propose an efficient generalized chain regression-cum-chain ratio type estimator to estimate finite population mean of study variable under stratified extreme-cum-median ranked set sampling utilizing information on two auxiliary variables. Mean square error (MSE) of the proposed generalized estimator is derived up to first order of approximation. The applications of the proposed estimator under symmetrical and asymmetrical probability distributions are discussed using simulation study and real-life data set for comparisons of efficiency. It is concluded that the proposed generalized estimator performs efficiently as compared to some existing estimators. It is also observed that the efficiency of the proposed estimator is directly proportional to the correlations between the study variable and its auxiliary variables.

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Exponential Type Estimator for the Population Mean Under Ranked Set Sampling

Journal of Statistics Advances in Theory and Applications ◽

10.18642/jsata_7100122173 ◽

2021 ◽

Vol 25 (1) ◽

pp. 1-12

Author(s):

Khalid Ul Islam Rather ◽

◽

Cem Kadilar ◽

Keyword(s):

Exponential Type ◽

Ranked Set Sampling ◽

Ratio Estimator ◽

Population Mean

We propose a new exponential type estimator for the population mean by adapting the estimator suggested by Kadilar [12] to the Ranked Set Sampling (RSS). Theoretically and numerically, we show that the proposed exponential type estimator is more efficient than the classical ratio estimator in the RSS and the estimator of Kadilar et al. [11].

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