Exponential-type estimators of the mean of a sensitive variable in the presence of nonsensitive auxiliary information

Nursel Koyuncu; Sat Gupta; Rita Sousa; DM Group Author

doi:10.1080/03610918.2012.737492

Exponential-type estimators of the mean of a sensitive variable in the presence of nonsensitive auxiliary information

Nursel Koyuncu, Sat Gupta, Rita Sousa, DM Group Author

DM - Departamento de Matemática

Research output: Contribution to journal › Article

19 Citations (Scopus)

Abstract

Sousa et al. and Gupta et al. suggested ratio and regression-type estimators of the mean of a sensitive variable using nonsensitive auxiliary variable. This article proposes exponential-type estimators using one and two auxiliary variables to improve the efficiency of mean estimator based on a randomized response technique. The expressions for the mean squared errors (MSEs) and bias, up to first-order approximation, have been obtained. It is shown that the proposed exponential-type estimators are more efficient than the existing estimators. The gain in efficiency over the existing estimators has also been shown with a simulation study and by using real data.

Original language	English
Pages (from-to)	1583-1594
Journal	Communications In Statistics-Simulation And Computation
Volume	43
Issue number	7
DOIs	https://doi.org/10.1080/03610918.2012.737492
Publication status	Published - 2014

Keywords

Absolute relative bias
Exponential estimator
Mean squared error
Randomized response technique
Regression estimator

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title = "Exponential-type estimators of the mean of a sensitive variable in the presence of nonsensitive auxiliary information",

abstract = "Sousa et al. and Gupta et al. suggested ratio and regression-type estimators of the mean of a sensitive variable using nonsensitive auxiliary variable. This article proposes exponential-type estimators using one and two auxiliary variables to improve the efficiency of mean estimator based on a randomized response technique. The expressions for the mean squared errors (MSEs) and bias, up to first-order approximation, have been obtained. It is shown that the proposed exponential-type estimators are more efficient than the existing estimators. The gain in efficiency over the existing estimators has also been shown with a simulation study and by using real data.",

keywords = "exponential estimator, mean squared error, randomized response technique, absolute relative bias, regression estimator, Absolute relative bias , Exponential estimator , Mean squared error , Randomized response technique , Regression estimator",

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AU - Koyuncu, Nursel

AU - Gupta, Sat

AU - Sousa, Rita

AU - DM Group Author

N1 - SCOPUSID:84893981655 WOS:000336526000004

PY - 2014

Y1 - 2014

N2 - Sousa et al. and Gupta et al. suggested ratio and regression-type estimators of the mean of a sensitive variable using nonsensitive auxiliary variable. This article proposes exponential-type estimators using one and two auxiliary variables to improve the efficiency of mean estimator based on a randomized response technique. The expressions for the mean squared errors (MSEs) and bias, up to first-order approximation, have been obtained. It is shown that the proposed exponential-type estimators are more efficient than the existing estimators. The gain in efficiency over the existing estimators has also been shown with a simulation study and by using real data.

AB - Sousa et al. and Gupta et al. suggested ratio and regression-type estimators of the mean of a sensitive variable using nonsensitive auxiliary variable. This article proposes exponential-type estimators using one and two auxiliary variables to improve the efficiency of mean estimator based on a randomized response technique. The expressions for the mean squared errors (MSEs) and bias, up to first-order approximation, have been obtained. It is shown that the proposed exponential-type estimators are more efficient than the existing estimators. The gain in efficiency over the existing estimators has also been shown with a simulation study and by using real data.

KW - exponential estimator

KW - mean squared error

KW - randomized response technique

KW - absolute relative bias

KW - regression estimator

KW - Absolute relative bias

KW - Exponential estimator

KW - Mean squared error

KW - Randomized response technique

KW - Regression estimator

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DO - 10.1080/03610918.2012.737492

M3 - Article

VL - 43

SP - 1583

EP - 1594

JO - Communications In Statistics-Simulation And Computation

JF - Communications In Statistics-Simulation And Computation

SN - 0361-0918

IS - 7

ER -