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11. A NEW CLASS OF EXPONENTIAL RATIO TYPE ESTIMATORS IN RANKED SET SAMPLING by Ayeleso T.O., Ajayi A.O., Mabosanyinje A. and Ogunsanya B.G. Volume 57(June – July 2020 Issue)
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A NEW CLASS OF EXPONENTIAL RATIO TYPE ESTIMATORS IN RANKED SET SAMPLING

Ayeleso T.O., Ajayi A.O., Mabosanyinje A. and Ogunsanya B.G.

Central Department of Statistics, Ogun State Ministry of Finance.

Department of Statistics, Federal University of Agriculture, Abeokuta.

Department of Statistics and Mathematics, Moshood Abiola Polytechnic, Abeokuta, Ogun State.

Abstract

Ranked set sampling (RSS) gives an advantage in deriving an unbiased estimator for population parameters with some noticeable increase in efficiency. This study presents a new class of exponential ratio type estimators in ranked set sampling (RSS) and compared with an existing class of modified exponential ratio estimators in simple random sampling (SRS). The data set used in this paper is the data on enrolment of students (variable of interest) and staff strength (auxiliary variable) in secondary schools in Egba zone of Ogun State in 2015. The zone had 89 schools and a 3cycle ranked set sample of size 27 was selected. The descriptive statistics of the data set were obtained for the estimation of population ratio. The mean square errors (MSEs) for both the proposed estimators and Singh estimators were determined to obtain the efficiencies of the proposed estimators. The population mean for student enrolment and staff strength were 1581.1 and 66.44 respectively which gave a population ratio of 23.80. The MSEs of Singh estimators were 276,587.7; 269,512.8; 253,601.6; 271,224.3; 274,429.9; 267,024.4; 253,020.3; 246,060.8; 271,080.4 and 274,957.9 while those of proposed estimators were 241,662; 234,759.1; 219,234.8; 236,429; 239,556.7; 232331.2; 218,667.5; 211,887.3; 236,288.6 and 240,071.9 respectively. The MSEs for the members proposed class of estimators were found to be smaller than those of Singh class of estimators, hence they are more efficient estimators.

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