In this paper exponential ratio and exponential product type estimators using two
auxiliary variables are proposed for estimating unknown population variance Sy2. Problem is
extended to the case of two-phase sampling. Theoretical results are supported by an empirical
This study proposes improved chain-ratio type estimator for
estimating population mean using some known values of population
parameter(s) of the second auxiliary character. The proposed estimators have
been compared with two-phase ratio estimator and some other chain type
estimators. The performances of the proposed estimators have been
supposed with a numerical illustration.
Optimum Statistical Test Procedure
In practice, the information regarding the population proportion possessing certain
attribute is easily available see Jhajj et.al. (2006). For estimating the population mean Y
of the study variable y, following Bahl and Tuteja (1991), a ratio-product type
exponential estimator has been proposed by using the known information of population
proportion possessing an attribute (highly correlated with y) in simple random sampling.
The expressions for the bias and the mean-squared error (MSE) of the estimator and its
minimum value have been obtained. The proposed estimator has an improvement over
mean per unit estimator, ratio and product type exponential estimators as well as Naik
and Gupta (1996) estimators. The results have also been extended to the case of two
phase sampling. The results obtained have been illustrated numerically by taking some
empirical populations considered in the literature.
In this paper we have proposed an almost unbiased ratio and product type
exponential estimator for the finite population mean Y-bar. It has been shown that Bahl and
Tuteja (1991) ratio and product type exponential estimators are particular members of the
proposed estimator. Empirical study is carried to demonstrate the superiority of the
It is well recognized that the use of auxiliary information in sample survey design
results in efficient estimators of population parameters under some realistic conditions.
Out of many ratio, product and regression methods of estimation are good examples in
this context. Using the knowledge of kurtosis of an auxiliary variable Upadhyaya and
Singh (1999) has suggested an estimator for population variance. In this paper, following
the approach of Singh and Singh (1993), we have suggested almost unbiased ratio and
product-type estimators for population variance.
Authors: Florentin Smarandache
Comments: 9 pages
This article presents several alternatives to Pearson's correlation coefficient
and many examples. In the samples where the rank in a discrete variable counts more
than the variable values, the mixture of Pearson's and Spearman's gives a better result.