Hilal A. Lone

Estimation of population variance in simple random sampling

Abstract In this paper, an estimator in simple random sampling has been proposed to estimate the population variance. Conditions under which the proposed estimator is more efficient than usual unbiased estimator and estimators given by Isaki (1983), Kadilar and Cingi (2006), Upadhyaya and Singh (1999), Singh and Solanki (2013), Singh et al. (1973), Das and Tripathi (1978), Singh et al.(1988), Prasad and Singh (1992), Garcia and Cebrian (1996) , Upadhyaya and Singh (2001) and Shabbir and Gupta (2006) are obtained. The bias and mean squared error of the proposed estimator is obtained up to the first degree of approximation. To judge the merits of the proposed estimator over other estimators, we consider three numerical examples.

Generalized ratio-cum-product type exponential estimator in stratified random sampling

ABSTRACT In this article, we propose a generalized ratio-cum-product type exponential estimator for estimating population mean in stratified random sampling. Asymptotic expression of the bias and mean squared error of the proposed estimator are obtained. Asymptotic optimum estimator in the proposed estimator has been obtained with its mean squared error formula. Conditions under which the proposed estimator is more efficient than usual unbiased estimator, combined ratio and product type estimators, Singh et al. (2008) estimators and Tailor and Chouhan (2014) estimator are obtained. An empirical study has also been carried out.

Efficient separate class of estimators of population mean in stratified random sampling

ABSTRACT In this paper, efficient class of estimators for population mean using two auxiliary variates is suggested. It has been shown that the suggested estimator is more efficient than usual unbiased estimator in stratified random sampling, usual ratio and product-type estimators, Tailor and Lone (2012, 2014) estimators, and other considered estimators. The bias and mean-squared error of the suggested estimator are obtained up to the first degree of approximation. Conditions under which the suggested estimator is more efficient than other considered estimators are obtained. An empirical study has been carried out to demonstrate the performances of the suggested estimator.

Generalized Ratio-Cum-Product Type Estimator of Finite Population Mean in Double Sampling for Stratification

This paper addressed the problem of estimation of finite population mean in double sampling for stratification. This paper proposed a generalized ratio-cum-product type estimator of population mean. The bias and mean square error of the proposed estimator has been obtained upto the first degree of approximation. A particular member of the proposed generalized estimator was identified and studied from a comparison point of view. It is observed that the identified particular estimator is more efficient than usual unbiased estimator and Ige and Tripathi (1987) estimators. An empirical study was conducted in support of the theoretical findings.

New alternatives to ratio estimators of population variance in sample surveys

In this paper we propose alternative estimators to ratio estimators given by Kadilar and Cingi (2006) and a class of estimators for population variance using the same approach adopted by Srivenkataramana (1980). Conditions under which the proposed estimators are more efficient than usual unbiased estimator, Isaki (1983) estimator, Biradar and Singh (1994) estimator and the estimators given by Kadilar and Cingi (2006) are obtained. The exact expressions for biases and mean squared errors of the proposed estimators are obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators. Also following Searls (1964), generalized version of the proposed class of estimators is also studied with its properties.