A general class of calibration estimators under stratified random sampling in presence of various kinds of non-sampling errors

Singh, G. N.; Bhattacharyya, D.; Bandyopadhyay, Arnab

doi:10.1080/03610918.2020.1855447

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Many authors have studied the effect of ME along with the estimation of population parameters. One may refer to [13][14][15][16][17][18]. Several researchers like [19][20][21][22][23][24] contribute to the variance estimation of the concerned variable in the presence of ME.…”

Section: Introductionmentioning

confidence: 99%

Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys

Qureshi,

Alamri,

Riaz

et al. 2023

PLoS ONE

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The present study suggested memory-type ratio and product estimators for variance estimation in the presence of measurement errors. We applied the exponentially weighted moving averages statistic which simultaneously utilizes the current and prior information for better estimation in surveys based on the time-scale. The expressions of approximate mean square errors of memory-type estimators are derived using Taylor series up to first order. Mathematical conditions are also obtained for which the suggested memory-type ratio and product estimators perform better than the conventional ratio and product estimators. The efficiency of the proposed estimators is observed using an extensive simulation study in the presence of measurement errors. A real data application is also carried out to support the mathematical expressions. From the results, it is shown that the use of prior sample information significantly increased the efficiency of the proposed estimators.

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Section: Introductionmentioning

confidence: 99%

Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys

Qureshi,

Alamri,

Riaz

et al. 2023

PLoS ONE

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show abstract

“…Recent advances in calibration techniques, as demonstrated by 21 , have focused on a class of calibration estimators under stratified random sampling in the presence of various kinds of non-sampling errors 22 . Explored calibration estimation for ratio estimators in stratified sampling for proportion allocation, and 23 further advanced the finite population distribution function estimation with the dual use of auxiliary information under simple and stratified random sampling 5 .…”

Section: Introductionmentioning

confidence: 99%

“…Its objective is to devise unbiased estimation procedures with minimal dispersion, leveraging auxiliary variables. Subsequent researchers, exemplified by 19 and 20 , have fine-tuned and extended calibration estimation procedures, striving to minimize the divergence between initial and final weights while adhering to calibration equations and constraints.Recent advances in calibration techniques, as demonstrated by 21 , have focused on a class of calibration estimators under stratified random sampling in the presence of various kinds of non-sampling errors 22 . Explored calibration estimation for ratio estimators in stratified sampling for proportion allocation, and 23 further advanced the finite population distribution function estimation with the dual use of auxiliary information under simple and stratified random sampling 5 .…”

mentioning

confidence: 99%

A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling

Pandey,

Singh,

Zaman

et al. 2024

Sci Rep

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This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems.

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A Class of Estimators for Estimation of Population Mean Under Random Non-response in Two Phase Successive Sampling

Basit,

Masood,

Bhatti

2023

J Stat Theory Appl

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This paper presents some efficient classes of estimators of population mean on current occasion in the presence of random non-response under a two-phase successive sampling set-up. The suggested classes of estimators are proposed for simple random sampling under various situations of non-response. The properties of proposed estimators have been discussed up to first order of approximation. The efficiency of the presented estimators has been contrasted with the estimators for the complete response scenarios. Two real and two artificially generated data sets are used. The efficacy of the proposed classes of estimators over the existing estimators is checked theoretically and empirically. The numerical comparison supports the proposed estimators.

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A general class of calibration estimators under stratified random sampling in presence of various kinds of non-sampling errors

Cited by 5 publications

References 22 publications

Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys

Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys

A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling

A Class of Estimators for Estimation of Population Mean Under Random Non-response in Two Phase Successive Sampling

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