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This paper addresses the problem of estimating the finite population variance using auxiliary information in sample surveys. Motivated by (Singh and Vishwakarma, 2009) some estimators of finite population variance have been suggested along with their properties in simple random sampling. The theoretical conditions under which the proposed estimators are more efficient than usual unbiased, usual ratio and (Singh et al., 2009) estimators have been obtained. Numerical illustrations are given in support of the present study., Statistica, Vol 74, No 1 (2014)

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.

We have addressed the problem of estimation of finite population variance using known valuesof quartiles of an auxiliary variable. Some ratio type estimators have been proposed with their properties insimple random sampling. The suggested estimators have been compared with the usual unbiased and ratioestimators. In addition, an empirical study is also provided in support of theoretical findings.Variation is present everywhere in our day to day life. It is law of nature that no two things or individuals are exactly alike. For instance, a physician needs a full understanding of variation in the degree of human blood pressure, body temperature and pulse rate for adequate prescription. A manufacture needs constant knowledge of the level of variation in peoples reaction to his product to be able to known whether to reduce or increase his price, or improve the quality of his product. An agriculturist needs an adequate understanding of variations in climate factors especially from place to place (or time to time) to be able to plan on when, how and where to plant his crop. Many more situations can be encountered in practice where the estimation of population variance of the study variable y assumes importance. In survey sampling, known auxiliary information is often used at the estimation stage to increase the precision of the estimators of population variance. For these reasons various authors such as Singh and Solanki (2009-2010), Tailor and Sharma (2012), Solanki and Singh (2013), Singh and Solanki (2013a, b), Subramani and Kumarapandiyan (2013a, b) and Yadav and Kadilar (2013a, b) have paid their attention towards the improved estimator of population variance of the study variable y using information on the known parameters of the auxiliary variable x such as mean, variance, coefficient of skewness, coefficient of kurtosis, correlation coefficient between the study variable y and the auxiliary variable x etc. Recently Subramani and Kumarapandiyan (2012a, b) have considered the problem of estimating the population variance of study variable y using information on variance, quartiles, inter-quartile range, semi-quartile range and semi-quartile average of the auxiliary variable x. In this paper our quest is to estimate the unknown population variance of study variable y by improving the estimators suggested by Subramani and Kumarapandiyan (2012a, b) using same information on an auxiliary variable x. Let U = (U1, U2,..., UN ) be finite population of size N and (y, x) are (study, auxiliary) variables taking values (yi , xi) respectively for the i-th unit Ui of the finite population U. Let a simple random sample (SRS) of size n be drawn without replacement (WOR) from the finite population U. The usual unbiased estimator s 2 y and the estimators of the population variance due to Isaki (1983) and Subramani and Kumarapandiyan (2012a, b) are given in the Table 1 along with their biases and mean squared errors (MSEs).

When open or direct surveys are about sensitive matters (e.g. gambling habits, addiction to drug and others intoxicants, alcoholism, proneness to tax invasion, induced abortions, drunken driving, history of past involvement in crimes, and homosexuality), non-response bias and response bias become serious problems because people oftentimes do not wish to give correct information. To reduce non-response and response bias, various alternative approaches have been proposed, for example a randomized response survey technique, or a mixed randomized response model using simple random sampling with a replacement sampling scheme that improves the privacy of respondents, proposed by authors Kim and Warde. In this paper we have suggested an alternative to Kim and Warde's mixed randomized response model to estimate the proportion of qualitative sensitive variable under the conditions presented in both the cases of completely truthful reporting and less than completely truthful reporting by the respondents. Properties of the proposed randomized response model have been studied along with recommendations. We have also extended the proposed model to stratified random sampling. Numerical illustrations and graphs are also given in support of the present study.

This paper proposes two ratio and product-type estimators using transformation based on known minimum and maximum values of auxiliary variable. The biases and mean squared errors of the suggested estimators are obtained under large sample approximation. Conditions are obtained under which the suggested estimators are superior to the conventional unbiased estimator, usual ratio and product estimators of population mean. The superiority of the proposed estimators are also established through some natural population data sets

In the present study, we propose a general class of estimators for population mean of the study variable in the presence of non-response using auxiliary information under double sampling. The expression of mean squared error (MSE) of the proposed class of estimators is derived under double (two-stage) sampling. Some estimators are also derived from the proposed class by allocating the suitable values of constants used. Comparisons of the proposed strategy with the usual unbiased estimator and other estimators are carried out. The results obtained are illustrated numerically using an empirical sample considered in the literature.