Sachin Malik

An improved estimator using two auxiliary attributes

In the present study, we propose two new estimators for population mean using Kadilar and Cingi (2005) [2] and Lu et al. (2010) [9] estimators in the case when the information is available in form of attributes. Expressions for the MSEs of the proposed estimator are derived up to the first degree of approximation. The theoretical conditions have also been verified by a numerical example. It has been shown that the proposed estimators are more efficient than usual regression estimators.

Improved estimation of population variance using information on auxiliary attribute in simple random sampling

Singh and Kumar (2011) [19] suggested estimators for calculating population variance using auxiliary attributes. This paper proposes a family of estimators based on an adaptation of the estimators presented by Kadilar and Cingi (2004) [5] and Singh et al. (2007) [16], and introduces a new family of estimators using auxiliary attributes. The expressions of the mean square errors (MSEs) of the adapted and proposed families are derived. It is shown that adapted estimators and suggested estimators are more efficient than Singh and Kumar (2011) [19] estimators. The theoretical findings are supported by a numerical example.

Some Improved Multivariate-Ratio-Type Estimators Using Geometric and Harmonic Means in Stratified Random Sampling

Auxiliary variable is commonly used in survey sampling to improve the precision of estimates. Whenever there is auxiliary information available, we want to utilize it in the method of estimation to obtain the most efficient estimator. In this paper using multiauxiliary information we have proposed estimators based on geometric and harmonic mean. It was also shown that estimators based on harmonic mean and geometric mean are less biased than Olkin (1958) and Singh (1967) estimators under certain conditions. However, the MSE of Olkin (1958) estimator and geometric and harmonic estimators are same up to the first order of approximations.

Estimation of population mean using information on auxiliary attribute in two-phase sampling

In this paper, we have proposed an estimator for finite population mean using auxiliary information in form of attributes under double sampling. We have also derived the expression of mean squared error of the proposed estimator. The proposed estimator is compared with the simple mean per unit, usual double sampling ratio and product estimators. An empirical study is carried out to judge the merit of the proposed estimator.

A new estimator for population mean using two auxiliary variables in stratified random sampling

Abstract In this paper, we suggest an estimator using two auxiliary variables in stratified random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data.