Search

This paper considers the problem of estimating the finite population mean $\bar{Y}$ of the study variable using information on two auxiliary variables $(x,z)$ . A family of ratio-cum-product estimators for population mean $\bar{Y}$ has been suggested. It has been shown that the usual unbiased estimator $\bar{y}$ , ratio estimator, product estimator, dual to ratio estimator and dual to product estimator due to Srivenkatramana (1980) and Bandyopadhyaya (1980), Singh et al's (2005, 2011) estimator, Tailor et al's (2012) estimator, Vishwakarma et al's (2014) estimator and Vishwakarma and Kumar (2015) estimator are members of the suggested family of estimators. In addition to these estimators, various unknown estimators are shown to be the member of the suggested family of estimators. The bias and mean squared error of the proposed family are obtained under large sample approximation. Efficiency comparisons are made to demonstrate the performance of the suggested family over other existing estimators. An empirical study is carried out in support of the present study.

This paper advocates the problem of estimating the population mean of the study variable y using the information on two auxiliary variables x and z. We have suggested the family of chain ratio exponential type estimators in two-phase (or double) sampling. The bias and mean squared error (MSE) are obtained upto the first order of approximation. The suggested class of estimators is more efficient than the two-phase ratio estimator, Chand’s (1975) chain-type ratio estimator and Singh and Vishwakarma’s (2007) estimator in two-phase (or double) sampling. An empirical study is given to justify the superiority of the proposed estimator., Statistica, Vol 73, No 2 (2013)

This paper suggested a class of estimators for the population mean of the study variable using information on an auxiliary variable with its properties under large sample approximation. The asymptotic optimum estimator in the proposed class has been identified with its properties. In addition, some existing estimators have been founded members of proposed class. It has been identified theoretically that the proposed class of estimators is better than the some traditional methods of estimation. An empirical study is carried out to judge the merits of proposed class over other competitors by using two natural population data sets., Statistica; Vol 72, No 4 (2012); 415-429

This paper addresses the problem of estimating the population mean of the study variate Y using information on two auxiliary variables X1 and X2 . A ratio-cum-product type exponential estimator has been suggested and its bias and mean squared error have been derived under large sample approximation. An almost unbiased ratio-cum-product type exponential estimator has also been derived by using Jackknife technique envisaged by Quenouille (1956). A generalized version of the ratio-cum-product exponential estimator has also been given along with its properties. Numerical illustration is given in support of the present study., Statistica; Vol 69, No 4 (2009); 299-310

In this paper a double (or two-phase) sampling version of (Singh and Tailor, 2005) estimator has been suggested along with its properties under large sample approximation. It is shown that the estimator due to (Kawathekar and Ajgaonkar, 1984) is a member of the proposed class of estimators. Realistic conditions have been obtained under which the proposed estimator is better than usual unbiased estimator, usual double sampling ratio ( tRd ) product ( tPd ) estimators and (Kawathekar and Ajgaonkar, 1984) estimator. This fact has been shown also through an empirical study., Statistica; Vol 72, No 1 (2012); 111-126

This paper considers the problem of estimating the reciprocal of the mean of the Inverse Gaussian distribution when a prior estimate or guessed value λ0 of the shape parameter λ is available. We have proposed a class of estimators with its mean squared error formula. Realistic conditions are obtained in which the estimator is better than usual estimator, uniformly minimum variance unbiased estimator (UMVUE) and the minimum mean squared error estimator (MMSE). Numerical illustrations are given in support of the present study., Statistica; Vol 68, No 2 (2008); 201-216

In this paper, an improved new additive model has been proposed. The proposed model is found to be more efficient than the randomized response models studied by Gjestvang and Singh (2009) and Singh (2010). The relative efficiency of the proposed model has been studied with respect to the Gjestvang and Singh (2009) and Singh (2010) model. It is found that the envisaged model is superior to those additive models earlier considered by Gjestvang and Singh (2009) and Singh (2010) under certain realistic conditions. Numerical illustrations are also given in support of the present study.