Model-Assisted Higher-Order Calibration of Estimators of Variance

Farrell, Patrick J.; Singh, Sarjinder

doi:10.1111/j.1467-842x.2005.00402.x

Cited by 30 publications

(6 citation statements)

References 12 publications

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“…Tracy et al [3] introduced a pair of constraints that use first and second-order moments of the auxiliary variables to obtain calibration weights for estimating the population mean in stratified sampling. Farrell and Singh [4] in (2005), Arnab and Singh [5] in (2005), and Estevao and Säarndal [6] in (2006) have also given some calibration estimators. Kim et al [7] proposed a calibration approach for the ratio estimators in stratified sampling design.…”

Section: Original Research Articlementioning

confidence: 99%

Separate Ratio Estimator Using Calibration Approach for the Population mean in Stratified Random Sampling

Khare

Singh²,

Mishra³

2022

AJPAS

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In this paper we investigate the problem of estimation of finite population mean using auxiliary information. The aim of this paper is to modify a separate ratio estimator by using calibration approach for the population mean using stratified random sampling. We use the calibration approach to improve the precision of estimate of the population parameter by incorporating auxiliary information. We use the chi-square distance function to minimize the distance between the original design weight and the new design weight. We use two constraints called calibration equations to find the new calibration weights. We use the Taylor’s linearization technique to find the approximate mean square (MSE) of the proposed estimator up to the first degree of approximation. We conduct a simulation study to assess the efficiency of the proposed calibration estimator based on an artificially generated data set. An empirical study has also been conducted to judge the merits of the proposed estimator, which shows that the proposed estimator performs better than the proposed estimator [9] and the usual separate ratio estimator.

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Section: Original Research Articlementioning

confidence: 99%

Separate Ratio Estimator Using Calibration Approach for the Population mean in Stratified Random Sampling

Khare

Singh²,

Mishra³

2022

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 definition of calibration is given by Deville and Sarndal (1992). There are many studies to make an improvement calibration techniques of population parameters such as Arnab and Singh (2005), Estevao and Sarndal (2000), Farrell and Singh (2005), Kim and Park (2010). In the stratified random sampling, calibration approach is used to get optimum strata weights.…”

Section: Introductionmentioning

confidence: 99%