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This paper addresses the problem of estimating the population mean Y... of the study variable Y using information on population parameters associated with the auxiliary attribute Φ. We have suggested a class of estimators of population Y... mean using auxiliary attribute. Expressions of bias and mean squared error of the suggested class of estimators have been derived up to first order of approximation. Optimum condition is obtained at which the proposed class of estimators has minimum mean squared error. Theoretical findings are also supported through the aid of a numerical illustration. [ABSTRACT FROM AUTHOR]

In this paper we consider a two parameter ratio-product-ratio estimator for estimating population mean in case of post stratification following the estimator due to Chami et al (2012). The bias and mean squared error of proposed estimator are obtained to the first degree of approximation. We derive conditions under which the proposed estimator has smaller mean squared error than the sample mean psy, ratio estimator) (ps R y and product estimators P(ps) y. Empirical studies gives insight on the magnitude of the efficiency of the estimator developed. [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]

This paper aims to discuss the problem of estimating the population mean Y ¯ of the study variable y using information on an auxiliary variable x in circular systematic sampling (CSS) scheme along with the non-response problem. We have suggested a generalized family of estimators for population mean Y ¯ of y based on auxiliary variable x. We have derived the expressions for bias and mean squared error of the proposed family of estimators upto first order of approximation. Optimum conditions are obtained in which the suggested family of estimators has the least mean squared error. It has been shown that the proposed family of estimators is better than usual unbiased estimator, ratio estimator and the regression estimator. An empirical study is provided to show that the suggested family of estimators is better than the ratio and regression estimators. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the population mean using auxiliary information in sample surveys. A class of dual to ratio estimators has been defined. Exact expressions for bias and mean squared error of the suggested class of dual to ratio estimators have been obtained. In particular, properties of some members of the proposed class of dual to ratio estimators have been discussed. It has been shown that the proposed class of estimators is more efficient than the sample mean, ratio estimator, dual to ratio estimator and some members of the suggested class of estimators in some realistic conditions. Some numerical illustrations are given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the population mean of the sensitive variable using auxiliary information in sample surveys. We have suggested a class of ratio-cum-ratio-type exponential estimator for the population mean of the sensitive variable based on auxiliary variable. We have derived the bias and mean squared error of the proposed class of estimators up to first and second order of approximation. Conditions are obtained in which the suggested class of estimators is more efficient than the usual unbiased estimators, ratio estimator due to Sousa et al. (2010) and ratio-type-exponential estimator. Optimum estimator in the proposed class of estimators is also obtained. Numerical study is carried out in the support of the present study. [ABSTRACT FROM AUTHOR]

In this paper, we have suggested a ratio-ratio-type exponential estimator for finite population mean in double sampling for stratification (DSS). To the first order of approximation the bias and mean squared error of the suggested estimator are obtained and compared theoretically and numerically both with unbiased estimator, ratio and product-type estimator and ratio and product-type exponential estimators. An empirical analysis is also being performed in support of the study. [ABSTRACT FROM AUTHOR]

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 suggests a two-parameter ratio-product-ratio type exponential estimator for a finite population mean in simple random sampling without replacement (SRSWOR) following the methodology in the studies of Singh and Espejo (2003) and Chami et al (2012). The bias and mean squared error of the suggested estimator are obtained to the first degree of approximation. The conditions are obtained in which suggested estimator is more efficient than the sample mean, classical ratio and product estimators, ratio-type and product type exponential estimators. An empirical study is given in support of the 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 the problem of estimating the finite population mean Y of the study variable using information on two auxiliary variables (x, z) . A family of ratio-cum-product estimators for population mean Y has been suggested. It has been shown that the usual unbiased estimator 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 aTs (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. [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]

This article addresses the problem of estimating the proportion πS of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is more efficient than Kim and Warde stratified randomized response model and Mangat model. [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 article suggests the class of estimators of population mean of study variable using various parameters related to an auxiliary variable with its properties in simple random sampling. It has been identified that the some existing estimator/classes of estimators are members of suggested class. It has been found theoretically as well as empirically that the suggested class is better than the existing methods. [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]

In this article, we propose a generalized ratio-cum-product type exponential estimator for estimating population mean in stratified random sampling. Asymptotic expression of the bias and mean squared error of the proposed estimator are obtained. Asymptotic optimum estimator in the proposed estimator has been obtained with its mean squared error formula. Conditions under which the proposed estimator is more efficient than usual unbiased estimator, combined ratio and product type estimators, Singh et al. (2008) estimators and Tailor and Chouhan (2014) estimator are obtained. An empirical study has also been carried out. [ABSTRACT FROM PUBLISHER]

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]

This paper suggested an alternative ratio-cum-product type class of estimators of population mean using exponentiation method in simple random sampling. Approximate bias and mean squared error formulae of the suggested class of estimators have been obtained up to the first order of approximation. Asymptotic optimum estimator in the suggested class of estimators has been obtained with its mean squared error formula. Regions of preferences have been obtained under which the suggested class of estimators has been better than the usual unbiased, ratio and product estimators and the estimators according to Singh and Agnihotri (2008). Some examples are cited with numerical study. [ABSTRACT FROM PUBLISHER]

This article suggests an improved class of estimators for estimating the general population parameter using information on an auxiliary variable. The properties of the suggested class of estimators have been studied under large sample approximation. The general results are then applied to estimate the population coefficient of variation of study variable using auxiliary information. An empirical study is given in support of the theoretical results. [ABSTRACT FROM PUBLISHER]

This article deals with the problem of estimation of the finite population mean using auxiliary information in the presence of random non response. Three different situations where random non response occurs either in study variate, or in auxiliary variate, or in both the variates, have been discussed. The asymptotically optimum estimators (AOEs) for each strategy are also identified. Expressions of biases and mean squared errors of the proposed estimators have been derived up to the first degree of approximation. Proposed estimators have been compared with the usual unbiased estimator, ratio estimator, and product estimator in the presence of random non response. Empirical studies are also carried out to show the performance of the proposed estimators over other estimators. [ABSTRACT FROM AUTHOR]

This paper suggested the ratio-type and product-type exponential estimators of the population mean of a study variable through predictive approach using Bahl and Tuteja (1991) ratio-type and product-type exponential estimators as a predictor of the mean of the unobserved units of the population. Properties of the suggested estimators are studied up to first order of approximation in simple random sampling using information on an auxiliary variable. The theoretical conditions under which the suggested estimators are less biased and more efficient than the usual unbiased, ratio, product estimators and estimators due to Srivastava (1983) and Bahl and Tuteja (1991) have been obtained. In support of the theoretical study numerical illustration is also given and determined that the suggested estimators showed also an improvement over the classical estimators empirically. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of estimating the finite population mean using auxiliary information in simple random sampling. Firstly we have suggested a correction to the mean squared error of the estimator proposed by Gupta and Shabbir (2008). Later we have proposed a ratio type estimator and its properties are studied in simple random sampling. Numerically we have shown that the proposed class of estimators is more efficient than different known estimators including Gupta and Shabbir (2008) estimator. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the general population parameter in the presence of random non-response using an auxiliary variable in three different situations. A general family of estimators of the parameter has been suggested and its properties are studied. In particular, the problem of estimating the population coefficient of variation has been considered. [ABSTRACT FROM AUTHOR]

This article considers the problem of estimating the standard deviation σ in complete samples from normal population N(μ,σ2) when some a priori or guessed interval containing the parameter σ is available in addition to sample information. Three classes of shrinkage estimators for standard deviation are suggested and their properties are studied. Comparisons with the usual unbiased estimator and minimum mean squared error (MMSE) estimator have been made. Numerical computations in terms of percent relative efficiency and absolute relative bias indicate that the proposed estimators are preferable to the MMSE estimator in some guessed interval of the parameter space of σ. [Copyright &y& Elsevier]

In this paper we propose alternative estimators to ratio estimators given by Kadilar and Cingi (2006) and a class of estimators for population variance using the same approach adopted by Srivenkataramana (1980). Conditions under which the proposed estimators are more efficient than usual unbiased estimator, Isaki (1983) estimator, Biradar and Singh (1994) estimator and the estimators given by Kadilar and Cingi (2006) are obtained. The exact expressions for biases and mean squared errors of the proposed estimators are obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators. Also following Searls (1964), generalized version of the proposed class of estimators is also studied with its properties. [ABSTRACT FROM AUTHOR]