On the efficiency of ratio estimator over the regression estimator

Clement, Etebong P.; Enang, Ekaette Inyang

doi:10.1080/03610926.2015.1100741

Cited by 16 publications

(22 citation statements)

References 9 publications

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“…[20] first presented calibration estimators in survey sampling and calibration estimation has been studied by many Researchers in Survey Theory. A few key references include [21][22][23][24][25][26][27][28][29].…”

Section: Original Research Articlementioning

confidence: 99%

“…The most challenging limitation of the ratio estimation is that of deriving variance estimator that admits more than two auxiliary variables. Many authors have proposed various forms of multivariate ratio estimators in sample surveys [see 15,17,18,38] among others] but their estimator of variance admit only two auxiliary variables (that is , bivariate ratio estimators).To derive estimator of variance that would admits as many auxiliary variables as desired, this paper adapts the variance estimation approach developed by [29] for the univariate ratio calibration estimator to multivariate ratio method estimation.…”

Section: Estimator Of Variance For the Proposed Estimatormentioning

confidence: 99%

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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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Section: Original Research Articlementioning

confidence: 99%

Section: Estimator Of Variance For the Proposed Estimatormentioning

confidence: 99%

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

Clement

2020

AJPAS

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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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“…The calibration technique has also been utilized to develop design-based estimator under different sampling schemes like stratified random sampling, stratified random double sampling, two-stage sampling, etc. In this direction many authors like Deville and Sarndal (1992), Singh and Mohl (1996), Estevao and Sarndal (2000), Estevao and Sarndal (2002), Singh (2003), Tracy et al (2003), Kim et al (2007), Barktus and Pumputis (2010), Sud et al (2014), Clement and Enang (2016), Rao et al (2016) and Subzar et al (2018) have proposed estimators and studied their properties for estimating population mean under different calibration constraints in stratified random sampling. Tracy et al (2003) obtained calibration weights for population mean by using first and second order moments of auxiliary variable in stratified random sampling.…”

Section: Introductionmentioning

confidence: 99%

Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling

Audu

Singh

Khare

2021

Statistics in Transition New Series

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In this paper, two modified, design-based calibration ratio-type estimators are presented. The suggested estimators were developed under stratified random sampling using information on an auxiliary variable in the form of robust statistical measures, including Gini's mean difference, Downton's method and probability weighted moments. The properties (biases and MSEs) of the proposed estimators are studied up to the terms of firstorder approximation by means of Taylor's Series approximation. The theoretical results were supported by a simulation study conducted on four bivariate populations and generated using normal, chi-square, exponential and gamma populations. The results of the study indicate that the proposed calibration scheme is more precise than any of the others considered in this paper.

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On the efficiency of ratio estimator over the regression estimator

Cited by 16 publications

References 9 publications

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

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

Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling

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