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This paper advocates the problem of estimating the population mean Ȳ of the study variable y using information on two auxiliary variables (x, z). We have suggested a class of estimators for population mean Ȳ of the study variable y based on the knowledge of the population means (X, Z ) of auxiliary variables (x, z), respectively. The bias and mean squared error of the proposed class estimators are obtained under large sample approximation. We have obtained the optimum conditions at which the suggested class of estimators is more efficient than the usual unbiased estimator y , st ratio and product estimators and the one recently proposed by Tailor et al. (2012). An empirical study is carried out in the favour of present study. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimating the population varianceS2yof the study variableyusing the auxiliary information in sample surveys. We have suggested the (i) chain ratio-type estimator (on the lines of Kadilar and Cingi (2003)), (ii) chain ratio-ratio-type exponential estimator and their generalized version [on the lines of Singh and Pal (2015)] and studied their properties under large sample approximation. Conditions are obtained under which the proposed estimators are more efficient than usual unbiased estimators2yand Isaki (1893) ratio estimator. Improved version of the suggested class of estimators is also given along with its properties. An empirical study is carried out in support of the present study. [ABSTRACT FROM PUBLISHER]

This article considers the problem of estimating the finite population meanof the study variableyusing information on an auxiliary variablexin presence of non-response. We have suggested two-parameter ratio-product-ratio estimator in two different situations and their properties are studied under large sample approximation. We have presented comparisons of the proposed two-parameter ratio-product-ratio estimator with usual unbiased estimatorand ratio estimators (tR1,tR2). An empirical study is carried out in support of the present study. [ABSTRACT FROM AUTHOR]

This paper considers a generalized ratio-cum-product estimator of population mean in systematic sampling using auxiliary information in sample surveys. A large number of estimators of different parameters of the study variables based on auxiliary information can be viewed as a member of suggested class of estimators. The bias and mean squared error of the proposed class of estimator have been obtained under large sample Approximation. Optimum conditions are obtained for which the proposed class of estimators has minimum mean squared error. It has been proved theoretically that the suggested class of estimators at its optimum condition is more different than the regression estimator in systematic sampling. An empirical study is carried out in support of the present study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population varianceS2yof the study variableyusing auxiliary information in sample surveys. We have suggested a class of estimators of the population varianceS2yof the study variableywhen the population varianceS2xof the auxiliary variablexis known. Asymptotic expressions of bias and mean squared error (MSE) of the proposed class of estimators have been obtained. Asymptotic optimum estimators in the proposed class of estimators have also been identified along with its MSE formula. A comparison has been provided. We have further provided the double sampling version of the proposed class of estimators. The properties of the double sampling version have been provided under large sample approximation. In addition, we support the present study with aid of a numerical illustration. [ABSTRACT FROM AUTHOR]

In the present article, we have proposed some classes of estimators based on transformed auxiliary variable. The biases and mean squared errors (MSEs) of the proposed estimators have been obtained. The proposed estimators have been compared with simple mean estimator when there is no matching and the optimum estimator, which is a combination of the means of the matched and unmatched portion of the sample at the second occasion. Optimum replacement policy and the efficiency of the proposed estimators have been discussed. Theoretical results are well supported with an empirical study. [ABSTRACT FROM AUTHOR]

This paper intends to put emphasis on the role of two auxiliary variables on both the occasions to improve the precision of estimates at current (second)occasion in two-occasion successive sampling. There are different situations when (i) the information on the single auxiliary variablez1is readily available on both the occasions and positively correlated with the study variabley, (ii) the information on the single auxiliary variablez2is readily available on both the occasions and negatively correlated with the study variabley, (iii) the information on two auxiliary variables (z1,z2) are available on both the occasions, with one auxiliary variablez1is positively correlated and the other negatively correlated with the study variabley; these have been discussed through proposing three estimators (one in each situation) of the population meanon the current (second) occasion and analyzing their properties. The properties of the proposed estimators have been studied and compared with the sample mean estimator when there is no matching from the previous occasion and traditional successive sampling estimator, which is a linear combination of the means of the matched and unmatched portion of the sample at the current (second) occasion. Optimal replacement policy is discussed. Empirical studies are carried out to show the domination of the recommended estimators. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating a general parameter using information on an auxiliary variableX. We have suggested a class of exponential-type ratio estimators for the parameter and its properties are studied. It is identified that the estimators due to Upadhyaya et al. [Journal of Statistical Theory and Practice (2011), 5(2), 285–302] and Yadav and Kadilar [Revista Columbiana de Estadistica, (2013), 36(1), 145–152] are members of the proposed estimator. We have also shown that the suggested estimator is more efficient than the estimators of Upadhyaya et al. (2011) and Yadav and Kadilar (2013). Numerical illustration is provided in support of the present study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimation of the population mean on the current (second) occasion in two-occasion successive sampling. Utilizing the readily available information on several auxiliary variables on both occasions and the information on the study variable from the previous occasion, an estimation procedure of the population mean on the current occasion has been proposed. Theoretical properties of the proposed estimator have been investigated. Optimum replacement policy to the proposed estimator has been discussed. The proposed estimator has been compared empirically with the sample mean estimator, when there is no matching and the optimum estimator which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion. Appropriate recommendations have been made for practical applications. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a class of estimators of finite population variance using known values of parameters related to an auxiliary variable such as quartiles and its properties are studied in simple random sampling. The suggested class of ratio-type estimators has been compared with the usual unbiased, ratio estimators and the class of ratio-type estimators due to Singhet al.[Improved estimation of finite population variance using quartiles, Istatistik – J. Turkish Stat. Assoc. 6(3) (2013), pp. 166–121] and Solankiet al.[Improved ratio-type estimators of finite population variance using quartiles, Hacettepe J. Math. Stat. 44(3) (2015), pp. 747–754]. An empirical study is also carried out to judge the merits of the proposed estimator over other existing estimators of population variance using natural data set. It is found that the proposed class of ratio-type estimators ‘’ is superior to the usual unbiased estimatorand the estimators recently proposed by Singhet al.[Improved estimation of finite population variance using quartiles, Istatistik – J. Turkish Stat. Assoc. 6(3) (2013), pp. 166–121] and Solankiet al.[Improved ratio-type estimators of finite population variance using quartiles, Hacettepe J. Math. Stat. 44(3) (2015), pp. 747–754]. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of estimating the finite population variance using auxiliary information in presence of measurement errors. We have suggested a class of estimators and its properties are studied under large sample approximation. It has been shown that the usual unbiased estimator and the estimators due to Sharma and Singh [A generalized class of estimators for finite population variance in presence of measurement errors, Journal of Modern Applied Statistical Methods, (2013), 12(2), 231-241.] are members of the proposed class of estimators. An alternative expression of the mean squared error of one the estimator due to Sharma and Singh [A generalized class of estimators for finite population variance in presence of measurement errors, Journal of Modern Applied Statistical Methods, (2013), 12(2), 231-241.] is also provided. The relative performance of various estimators has been examined through an empirical study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population mean of the study variable y using information on an auxiliary variable x. A class of Exponential-Type Estimators has been suggested along with its properties under large sample approximation. It is identified that the usual Unbiased Estimator, Product-Type and Ratio-Type Exponential Estimators are members of the proposed class of Exponential-Type Estimators. It has been shown that the proposed class of Exponential-Type Estimators is more efficient than the usual Unbiased Estimator and some existing Estimators. An empirical study is carried out in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the population mean Y of the study variable y using information on auxiliary variable x in stratified random sampling. A class of estimators has been proposed. The bias and mean squared error have been obtained up to first degree of approximation. Optimum condition is obtained in which the proposed class of estimators has least mean squared error. We have also compared the proposed class of estimators with some existing estimators. An empirical study is carried out in support of the present study. [ABSTRACT FROM AUTHOR]