Auxiliary Information In Sample Surveys Research Articles

Weighted Strategy for Estimation of Finite Population Mean in Presence of Auxiliary Information

ABSTRACT In sample designs, it is commonly recognized that using auxiliary information significantly increases an estimator’s precision. This manuscript introduces an weighted strategy for computing the finite population mean using auxiliary information in sample surveys. The equations for the mean squared error (MSE) of the proposed estimator are derived under large sampling approximation. A graphical representation of efficiency comparison is provided to show the ranges where the proposed estimator performs more efficiently. Additionally, a numerical analysis is conducted to evaluate the performance of the suggested estimator using actual datasets.

Additive ratio type exponential estimator of finite population mean of sensitive variable using non-sensitive auxiliary information based on optional randomized response model

The appropriate use of auxiliary information in sample surveys increases the efficiency of estimator for parameter of interest. In this paper, we have proposed an exponential type estimator for the population mean of a sensitive study variable based on an optional randomized response model by using the known information on a non-sensitive auxiliary variable. Expressions for the bias and the mean square error (MSE) of the proposed estimator are derived, up to first order of approximation. For this proposed estimator, efficiency comparisons with the existing estimators have been carried out both theoretically and numerically. It has been shown that our proposed estimator perform better than the existing estimators based on the same optional randomized response model even for the small correlation between auxiliary variable and study variable. To support the results obtained, we have also studied the performance of the proposed exponential estimator using simulation technique.

A General Class of Dual to Ratio Estimator

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 estimator 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.

An Improved Family of Estimators of Finite Population Mean Using Information on an Auxiliary Variable in Sample Surveys

AbstractIn this paper we have suggested a family of estimators of the population mean using auxiliary information in sample surveys. The bias and mean squared error of the proposed class of estimators have been obtained under large sample approximation. We have derived the conditions for the parameters under which the proposed class of estimators has smaller mean squared error than the sample mean, ratio, product, regression estimator and the two parameter ratio-product-ratio estimators envisaged by Chami et al (2012). An empirical study is carried out to demonstrate the performance of the proposed class of estimators over other existing estimators.

A family of efficient estimators of the finite population mean in simple random sampling

ABSTRACTIn this paper an estimator of the finite population mean using auxiliary information in sample surveys has been proposed. The bias and mean squared error are obtained under large sample approximation. It has been shown that the proposed estimator performs better than some recently published estimators.

A study on the chain ratio-ratio-type exponential estimator for finite population variance

ABSTRACTThis paper considers the problem of estimating the population variance S2y of the study variable y using 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 estimator s2y and 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.

Improvement over variance estimation using auxiliary information in sample surveys

ABSTRACTThis paper addresses the problem of estimating the population variance S2y of the study variable y using auxiliary information in sample surveys. We have suggested a class of estimators of the population variance S2y of the study variable y when the population variance S2x of the auxiliary variable x is 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.

A Generalized Class of Ratio-Cum-Dual to Ratio Estimators of Finite Population Mean Using Auxiliary Information in Sample Surveys

A Generalized Class of Ratio-Cum-Dual to Ratio Estimators of Finite Population Mean Using Auxiliary Information in Sample Surveys

ESTIMATION OF FINITE POPULATION VARIANCE USING AUXILIARY INFORMATION IN SAMPLE SURVEYS

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.

A Class of Estimators of Population Variance Using Auxiliary Information in Sample Surveys

This article advocates the problem of estimating the population variance of the study variable using information on certain known parameters of an auxiliary variable. A class of estimators for population variance using information on an auxiliary variable has been defined. In addition to many estimators, usual unbiased estimator, Isaki's (1983), Upadhyaya and Singh's (1999), and Kadilar and Cingi's (2006) estimators are shown as members of the proposed class of estimators. Asymptotic expressions for bias and mean square error of the proposed class of estimators have been obtained. An empirical study has been carried out to judge the performance of the various estimators of population variance generated from the proposed class of estimators over usual unbiased estimator, Isaki's (1983), Upadhyaya and Singh's (1999) and Kadilar and Cingi's (2006) estimators.

Estimation of poverty measures with auxiliary information in sample surveys

The analysis of poverty measures has been receiving increased attention in recent years. This paper contributes to the literature by developing percentile ratio estimators based on the pseudo empirical likelihood method. In practice, variances of poverty measures could be not expressible by simple formulae and consequently other techniques should be used in the variance estimation stage. Assuming percentile ratios, resampling techniques are investigated in this paper. A numerical example based on data from the Spanish Household Panel Survey is taken up to illustrate how suggested procedures can perform better than existing ones. The effect of a model-misspecification on the proposed estimators is also evaluated by using simulated populations.

Effect of Measurement Errors on a Class of Estimators of Population Mean Using Auxiliary Information in Sample Surveys

Effect of Measurement Errors on a Class of Estimators of Population Mean Using Auxiliary Information in Sample Surveys

Some improved estimators of finite population quantile using auxiliary information in sample surveys

The problem of quantile estimation using quantiles Q x ( α) in which the order of the auxiliary variable is different from that of the main variable to be estimated, Q y ( β), is considered. Certain new estimators for the β-quantile have been proposed for any sampling design. The effect of this modification on the standard estimators, ratio, position, stratification, regression and difference type estimators which use the β-quantile of the auxiliary variable to estimate the β-quantile of the main variable, is studied. On the basis of properties derived and some simulation results, the efficiencies of these estimators are compared. It is shown that by the appropriate choice of the α order of the quantile, it is possible to obtain a considerable increase in precision with respect to standard estimators. In simple random sampling, a procedure for choosing the α value is proposed.

Estimation of the Population Standard Deviation Using Auxiliary Information

SYNOPTIC ABSTRACTThis paper proposes a class of estimators for estimating population standard deviation using auxiliary information in sample surveys. The large sample expressions for the bias and variance of the estimators are derived and the minimum variance obtained. An empirical study is carried out to demonstrate the performance of the constructed estimators over usual standard deviation estimator.

A new approach to use multivariate auxiliary information in sample surveys

A new approach to form multivariate difference estimator is suggested which does not require the knowledge of unknown population parameters as such. It gives minimum variance among the class of multivariate difference estimators. The performance of this estimator with respect to Des Raj's ( J. Amer. Statist. Assoc. 60 (1965), 270–277) multivariate difference estimator is illustrated. Using the information on two auxiliary variates, the robustness of Des Raj's estimator y d is studied empirically. Two new estimators to estimate population mean/total are developed on the same lines as that of y d. The performance of these estimators is studied for a wide variety of populations.

Unbiased estimators of finite population variance using auxiliary information in sample surveys

We present some unbiased estimators of the population variance in a finite population sample survey using the knowledge of population variance of an auxiliary character.Exact variance expressions for the proposed estimators are obtained and compared with usual unbiased estimator and the ratio estimator envisaged by Isaki (1983). Generalization of the proposed estimator is also suggested.

A class of estimators using auxiliary information in sample surveys

AbstractA large class of estimators is considered for the mean of a finite population using information on an auxiliary variable. It is shown that members of this class of estimators are asymptotically no more efficient than the linear regression estimator.