An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling

Enang, Ekaette Inyang; Clement, Etebong P.

doi:10.1007/s40304-018-00174-z

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The calibration weights are chosen to minimize a given distance measure (or loss function) and these weights satisfy the constraints related supplementary variable information. The concept of calibration estimation was introduced by Deville and Sarndal (1992) and a wealth of research, featuring scholars like Wu and Sitter (2001), Arnab and Singh (2005), Kim et al (2007), Sarndal (2007), Kim and Park (2010), Rao et al (2012), Clement et al (2014), Koyuncu and Kadilar (2016), Clement and Enang (2017), Clement (2021Clement ( , 2022, Inyang (2020, 2021), Enang and Clement (2020), has delved into calibration estimation. Tracy et al (2003) introduced the concept of calibration estimation to stratified double sampling using multi-parametric calibration weightings.…”

Section: International Journal Of Statistical Sciences Vol 24(1) 2024mentioning

confidence: 99%

On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling

Clement,

Enang

2024

Int. J. of Stat. Sci.

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This study introduces the concept of inverse exponentiation in formulating calibration weights in stratified double sampling and proposes a more improved calibration estimator based on Koyuncu and Kadilar (2014) calibration estimator. The variance of the proposed logarithmic calibration estimator has been derived under large sample approximation. Calibration asymptotic optimum estimator and its approximate variance estimator are derived for the proposed logarithmic calibration estimator. Results of empirical study showed that the proposed logarithmic calibration estimator performs better than the Koyuncu and Kadilar (2014) calibration estimator with appreciable gains in efficiency. Also, simulation study for the comparison of the proposed logarithmic estimator with a Global estimator [Generalized Regression (GREG) estimator ] proved the robustness of the proposed logarithmic calibration estimator and by extension the efficacy of inverse exponentiation in calibration weightings. Analysis and evaluation are presented. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 91-102

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Section: International Journal Of Statistical Sciences Vol 24(1) 2024mentioning

confidence: 99%

On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling

Clement,

Enang

2024

Int. J. of Stat. Sci.

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“…Several, related literature reviewed showed that tremendous work have been done on calibration estimation under the univariate ratio and univariate regression methods of estimation and much more on the multivariate regression estimation [ see [30][31][32], but the theory of calibration estimator for multivariate ratio estimation is not well known.…”

Section: Original Research Articlementioning

confidence: 99%

On the Improvement of Multivariate Ratio Method of Estimation in Sample Surveys by Calibration Weightings

Clement

2020

AJPAS

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The most challenging limitation of the ratio estimation is that of deriving variance estimator that admits more than two auxiliary variables. This paper introduces a new calibration weights that prompt the formulation of a multivariate ratio estimator by the calibration tuning parameter subject to a pooled-calibration constraint. Analytical framework for deriving variance estimator that admits as many auxiliary variables as desired is developed. The efficiency gains of the proposed estimator vis-a-vis the Generalized Regression (GREG) Estimator are studied through simulation. Simulation results proved the dominance of the new proposals over existing ones.

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“…Calibration estimation has been studied by many survey Statisticians. A few key references are [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Original Research Articlementioning

confidence: 99%

On the Estimation of Variance of Calibration Regression Estimators with Multiple Auxiliary Information

Clement

2020

AJPAS

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This paper introduces the concept of calibration estimators to Statistical Regression Estimation and proposes a multivariate calibration regression (M-REG) estimator of population mean in stratified random sampling. It develops a new approach to variance estimation that is more efficient in estimating populations with multiple auxiliary variables using the principle of analysis of variance (ANOVA). The relative performance of the new variance estimation method with respect to the estimation of variance of the proposed M-REG estimator is compared empirically with a corresponding global variance estimation method. Analysis and evaluation presented, proved the dominance of the suggested new approach to variance estimation.

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An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling

Cited by 9 publications

References 9 publications

On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling

On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling

On the Improvement of Multivariate Ratio Method of Estimation in Sample Surveys by Calibration Weightings

On the Estimation of Variance of Calibration Regression Estimators with Multiple Auxiliary Information

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