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Some linear regression type ratio exponential estimators for estimating the population mean based on quartile deviation and deciles

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Prasad S.
Statistics in Transition new series
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2020
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vol. 21
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issue 5
85-98
EN
This paper deals some linear regression type ratio exponential estimators for estimating the population mean using the known values of quartile deviation and deciles of an auxiliary variable in survey sampling. The expressions of the bias and the mean square error of the suggested estimators have been derived. It was compared with the usual mean, usual ratio (Cochran (1977)), Kadilar and Cingi (2004, 2006) and Subzar et al. (2017) estimators. After comparison, the condition which makes the suggested estimators more efficient than others is found. To verify the theoretical results, numerical results are performed on two natural population data sets.
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A Class of Regression Type Estimators in Survey Sampling

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Misra G. C., Yadav S. K., Shukla A. K.
Statistics in Transition new series
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2011
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vol. 12
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issue 3
579-586
EN
A class of linear regression models has been proposed for the estimation of population mean and total when information regarding auxiliary variate is available in survey sampling using regression method of estimation by introducing a new auxiliary variable z, which may also be a function of the auxiliary variable x. The proposed model leads to reduction in mean squared error as compared to ordinary regression method of estimation. The improvement has been demonstrated over ordinary regression estimator and also on ratio estimator with the help of an empirical example.
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