A General Procedure of Estimating Population Mean Using Information on Auxiliary Attribute (original) (raw)

Some Improved Estimators of Population Mean Using Information on Two Auxiliary Attributes

In this paper, we have studied the problem of estimating the finite population mean when information on two auxiliary attributes are available. Some improved estimators in simple random sampling without replacement have been suggested and their properties are studied. The expressions of mean squared error's (MSE's) up to the first order of approximation are derived. An empirical study is carried out to judge the best estimator out of the suggested estimators.

On Estimation of Poulation Mean Using Information on Auxiliary Attribute

Pakistan Journal of Statistics and Operation Research, 2013

We consider the problem of estimating the finite population mean when some information on auxiliary attribute is available. We obtain the mean square error (MSE) equation for the proposed estimators. It has been shown that the proposed estimator is better than Gupta (1996), Singh et al. (2008), Abd-Elfattah (2010) estimators. The results have been illustrated numerically by taking some empirical population considered in the literature.

An improved family of estimators of finite population mean based on the auxiliary attribute

Applied Mathematics and Computation, 2014

Recently, [3] proposed an efficient family of estimators for estimation of finite population mean using information on the auxiliary attribute. In this paper, we propose two improved estimators of finite population mean. The biases and mean squared errors of the proposed estimators are derived up to the first order of approximation. It is observed that the first proposed estimator is always better than the first family of estimators adapted by Koyuncu (2012) [3]. An empirical study is carried out to demonstrate the performance of the proposed estimators.

A Generalized Family of Estimators for Estimating Population Mean Using Two Auxiliary Attributes

This paper deals with the problem of estimating the finite population mean when some information on two auxiliary attributes are available. A class of estimators is defined which includes the estimators recently proposed by Malik and Singh (2012), Naik and Gupta (1996) and Singh et al. (2007) as particular cases. It is shown that the proposed estimator is more efficient than the usual mean estimator and other existing estimators. The study is also extended to two-phase sampling. The results have been illustrated numerically by taking empirical population considered in the literature.

A Family of Estimators of Population Mean Using Information on Auxiliary Attribute

2006

In practice, the information regarding the population proportion possessing certain attribute is easily available. So for estimating the population mean Y of study variable y, a family of estimators of Y has been proposed by using the known information of population proportion possessing an attribute (highly correlated with y). The expressions for the mean square error of the estimators of the proposed family and its minimum value have been obtained. It has been shown that the optimum estimator of the proposed family of estimators of Y is always better than the mean per unit estimator. The results have also been extended for the case of the double sampling design. The results obtained have been illustrated numerically by taking some empirical populations considered in the literature.

Efficient estimators of population mean using auxiliary attributes

Applied Mathematics and Computation, 2012

a b s t r a c t Abd-Elfattah et al. [1] suggested a set of estimators for calculating population mean using auxiliary attributes. This paper proposes a family of estimators based on an adaptation of the estimators presented by Koyuncu and Kadilar [2], and introduces a new family of exponential estimators using auxiliary attributes. The expressions of the mean square errors (MSEs) of the adapted and proposed families are derived in a general form. It is shown that the adapted version of the Koyuncu and Kadilar [2] estimators is always more efficient than that of Abd-Elfattah et al. [1]. Moreover, the new exponential estimators based on auxiliary attributes are more efficient than those of Koyuncu and Kadilar [2] and Abd-Elfattah et al. [1]. The theoretical findings are supported by a numerical example using original data.

An Efficient Class of Estimators for Finite Population Mean Using Auxiliary Variable

Thailand Statistician, 2022

We have proposed a general class of estimators for population mean using auxiliary variable in simple random sampling. Our estimator contains various estimators from literature and many more can be developed from it. The expressions for bias and mean square error (MSE) are derived. It is found that the estimator in question is better than the other available alternatives. The Monte-Carlo simulation is carried out in various cases to show the validity of the estimator.

On the Estimation of Population Mean Under Systematic Sampling Using Auxiliary Attributes

Oriental Journal of Physical Sciences, 2016

Naik and Gupta (1996), Singh et al. (2007) and Abd-Elfattah et al. (2010) introduced some estimators for estimating population mean using available auxiliary attributes under simple random sampling scheme. We adapt these estimators under systematic random sampling scheme using available auxiliary attributes. Further, a new family of estimators is proposed for the estimation of population mean under systematic random sampling scheme. The properties such as bias and mean square error of the proposed estimators are derived. From numerical illustration it is shown that proposed estimators are more efficient than the reviewed ones.

A generalized exponential-type estimator for population mean using auxiliary attributes

PLOS ONE

In this paper, we propose a generalized class of exponential type estimators for estimating the finite population mean using two auxiliary attributes under simple random sampling and stratified random sampling. The bias and mean squared error (MSE) of the proposed class of estimators are derived up to first order of approximation. Both empirical study and theoretical comparisons are discussed. Four populations are used to support the theoretical findings. It is observed that the proposed class of estimators perform better as compared to all other considered estimator in simple and stratified random sampling.

Improved Estimation of the Population Mean Using Known Parameters of an Auxiliary Variable

2011

An improved ratio-cum-product type estimator of the finite population mean is proposed using known information on the coefficient of variation of an auxiliary variate and correlation coefficient between a study variate and an auxiliary variate. Realistic conditions are obtained under which the proposed estimator is more efficient than the simple mean estimator, usual ratio and product estimators and estimators proposed by Singh and Diwivedi (