A new estimator using two auxiliary variables

Kadılar, Cem; Çıngı, Hülya

doi:10.1016/j.amc.2003.12.130

Cited by 71 publications

(47 citation statements)

References 2 publications

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“…Under different sample sizes and different datasets, we notice from the data given in Tables 1, 2, 3, and 4 that the proposed estimator of a finite population mean using two auxiliary variables is always more efficient than the estimators AD , KC , MS , and RE . Although the expressions of the estimators reported by Abu-Dayyeh et al, Kadilar and Cingi, and Malik and Singh are different, we note from the comparative study above that the minimum MSE values for the estimators reported by Abu-Dayyeh et al [7], Kadilar and Cingi [8], and Malik and Singh [9] are equal and have same expression. The regression estimator has a smaller MSE value than the three estimators noted above.…”

Section: Mse and Pre Values Of Different Estimators About Population mentioning

confidence: 77%

“…We compared the MSE of the proposed estimator with two auxiliary variables given in (27) with the MSE of the estimator reported by Abu-Dayyeh et al [7], as given in (4), Kadilar and Cingi [8], as given in (8), Malik and Singh [9], as given in (12), and the regression estimator, as given in (17), as follows:…”

Section: Comparison Of Pr With Some Existing Estimatorsmentioning

confidence: 99%

“…More recently, several authors have proposed efficient estimators of finite population mean using two variables or attributes, including, Abu-Dayyeh et al [7], Kadilar and Cingi [8], Malik and Singh [9], Sharma and Singh [10], and Muneer et al [11]. Although these studies are detailed and elaborated, the formulas of minimum MSE are not given, and the difference of minimum MSE values between these studies seems not to have been noticed.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we compare the estimators reported by Abu-Dayyeh et al [7], Kadilar and Cingi [8], and Malik and Singh [9] and introduce a new estimator with two auxiliary variables to estimate a finite population mean for the variable of interest. We obtained bias and mean square error (MSE) equations for the proposed estimator, and we compared the new estimator against those with relatively high efficiencies.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Estimator of a Finite Population Mean Using Two Auxiliary Variables and Numerical Application in Agricultural, Biomedical, and Power Engineering

2017

Mathematical Problems in Engineering

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To improve the efficiency of an estimator with two auxiliary variables, we propose a new estimator of a finite population mean under simple random sampling. The bias and mean square error expressions of the proposed estimator have been obtained. In a comparison study, we found that the new estimator was consistently better than those of Abu-Dayyeh et al., Kadilar and Cingi, and Malik and Singh, as well as the regression estimator using two auxiliary variables, and that the minimum MSE values of the previous three above reported estimators were equal. We used four numerical examples in agricultural, biomedical, and power engineering to support these theoretical results, thus enriching the theory of survey samples by the development of new estimators with two auxiliary variables.

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Section: Mse and Pre Values Of Different Estimators About Population mentioning

confidence: 77%

Section: Comparison Of Pr With Some Existing Estimatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Estimator of a Finite Population Mean Using Two Auxiliary Variables and Numerical Application in Agricultural, Biomedical, and Power Engineering

2017

Mathematical Problems in Engineering

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“…Using information on two auxiliary variables several modified ratio estimators have been proposed by linking together ratio estimators, product estimators and regression estimators in order to find better results. For a more detailed discussion on the ratio estimator and its modifications using two auxiliary variables , one may refer to Lu and Yan [16], Lu et al [17], Al-Hossain and Khan [6], Subramani and Prabavathy [34], Lu [15], Khare et al [14], Perri [20], Kadilar and Cingi [11] and [12], Singh and Tailor [24] and [25], Abu-Dayyeh et al [5], Srivastava and Jhajj [28], Srivastava [27], Raj [21] and Olkin [19].…”

Section: Introductionmentioning

confidence: 99%

Improved Ratio Estimators Using Some Robust Measures

Abid¹,

Nazir²,

Riaz³

et al. 2017

HJMS

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Estimation of population mean is of prime concern in many studies and ratio estimators are popular choices for it. It is a common practice to use conventional measures of location to develop ratio estimators using information on auxiliary variables. In this article, we propose a class of ratio estimators for a finite population mean using information on two auxiliary variables with the help of some non-conventional location measures. We have incorporated tri-mean, Hodges-Lehmann, mid-range and decile mean of the two auxiliary variables to serve the purpose. The properties associated with the proposed class of ratio estimators are evaluated using mean square error. We have presented efficiency comparisons of the proposed class of ratio estimators with other existing estimators under the optimal conditions. An empirical study is also included for illustration and verification purposes. From theoretical and empirical study, we observed that the proposed estimators perform better as compared to the usual ratio and the existing estimators consider in this study.

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On enhanced exponential‐cum‐ratio estimators using robust measures of location

Gulzar

Latif

Abid

et al. 2021

Concurrency and Computation

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Many studies are mainly concerned with the estimation of finite population mean and the well-known preferences for it are ratio estimators. This article offers a remedy for improvement in the estimation of a study variable, based on supplementary information related to dual auxiliary variables in the context of simple random sampling without replacement scheme. We proposed two new general classes of exponential-cum-ratio estimators to estimate a finite population mean utilizing dual auxiliary variables with suitable combinations of the conventional and nonconventional measures. The expression for the mean square error (MSE) and theoretical conditions for proposed classes have been obtained for evaluation purposes. The performance of the proposed classes has been compared with existing estimators in terms of MSE. It is revealed that proposed estimators are more efficient than usual and existing estimators considered in this article. The simulation and robustness studies are also part of this article. Moreover, a variety of real data sets is also considered for an empirical study to support the theoretical results.

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A new estimator using two auxiliary variables

Cited by 71 publications

References 2 publications

Efficient Estimator of a Finite Population Mean Using Two Auxiliary Variables and Numerical Application in Agricultural, Biomedical, and Power Engineering

Efficient Estimator of a Finite Population Mean Using Two Auxiliary Variables and Numerical Application in Agricultural, Biomedical, and Power Engineering

Improved Ratio Estimators Using Some Robust Measures

On enhanced exponential‐cum‐ratio estimators using robust measures of location

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