Estimation of population mean under probability proportional to size sampling with and without measurement errors

Sinha, R. R.; Khanna, Bharti

doi:10.1002/cpe.7023

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…An improved class of estimators has been obtained for the estimation of population mean based on Probability Proportional to size (PPS) sampling, with the help of two auxiliary variables (4) . The problem of estimating the population mean of the study variable with the help of auxiliary variables under varying probability techniques was focused with and without measurement errors (5) .…”

Section: Introductionmentioning

confidence: 99%

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

Garg,

Patel,

Pachori

2024

IJST

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Objectives: In this article, we addressed the problem of estimation of the finite population proportion under the Probability Proportional to Size (PPS) sampling technique, when the complete information is unavailable due to the presence of non-response. We developed calibrated estimators of the population proportion under PPS sampling in the presence of nonresponse based on the availability of auxiliary information. Methods: The expressions for the mean squared errors of the suggested estimators were developed to the first order of approximation. The developed estimators of the population proportion are compared with the design-based Horvitz-Thompson estimator and Horvitz-Thompson type calibration estimator which were obtained on the complete response units along with the design-based Hansen and Hurwitz type estimator in the presence of non-response. A Simulation study has also been conducted to support the performance of the developed estimators of population proportion with the help of two real datasets, by computing Percentage Absolute Relative Bias (%ARB) and Percentage Relative Root Mean Squared Error (%RRMSE) using R software. Findings: The simulation study supported the performance of the developed estimators of the finite population proportion based on %ARB and %RRMSE. The proposed calibration estimators of population proportion are more efficient than the other considered estimators in the presence of non-response. Novelty: The proposed new calibrated estimators have practical implications in the estimation of finite population proportions. Keywords: Auxiliary information, Calibration Approach, Nonresponse, Population proportion, PPS sampling

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Section: Introductionmentioning

confidence: 99%

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

Garg,

Patel,

Pachori

2024

IJST

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“…Latpate et al [34] discussed in detail probability proportional to the size sampling. Sohil et al [35] suggested optimum second call imputation in PPS sampling and Sinha and Khanna [36] discussed the estimation of the population mean under probability proportional to size sampling with and without measurement errors. Makela and Gelman [31] suggested Bayesian inference under cluster sampling with probability proportional to size.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

Ahmad

Zahid

Shabbir

et al. 2023

Mathematical Problems in Engineering

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In some situations, the population of interest differs significantly in size, for example, in a medical study, the number of patients having a specific disease and the size of health units may vary. Similarly, in a survey related to the income of a household, the household may have a different number of siblings, and then in such situations, we use probability proportional to size sampling. In this article, we have proposed an improved class of estimators for the estimation of population mean on the basis of probability proportional to size (PPS) sampling, using two auxiliary variables. The mathematical expressions of the bias and mean square error (MSE) are derived up to the first order of approximation. Four real datasets and a simulation study are conducted to assess the efficiency of the improved class of estimators. It is found from the real datasets and a simulation study, that the proposed generalized class of estimators produced better results in terms of minimum MSE and higher PRE, as related to other considered estimators. An empirical study is given to support the theoretical results. The theoretical study also demonstrates that the proposed generalized class of estimators outperforms the existing estimators.

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“…Sohil et al 31 recommended optimum second call imputation in PPS sampling. Sinha and Khanna 32 discussed the estimation of population mean under PPS sampling with and without measurement errors. Zangeneh and Little 33 discussed Bayesian inference for the finite population total from a heteroscedastic PPS.…”

Section: Introductionmentioning

confidence: 99%

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

Ahmad,

Shabbir,

Zahid

et al. 2023

Science Progress

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This article aims to suggest a new generalized class of estimators based on probability proportional to size sampling using two auxiliary variables. The numerical expressions for the bias and mean squared error (MSE) are derived up to the first order of approximation. Four actual data sets are used to examine the performances of a new improved generalized class of estimators. From the results of real data sets, it is examined that the suggested estimator gives the minimum MSE and the percentage relative efficiency is higher than all existing estimators, which shows the importance of the new generalized class of estimators. To check the strength and generalizability of our proposed class of estimators, a simulation study is also accompanied. The consequence of the simulation study shows the worth of newly found proposed class estimators. Overall, we get to the conclusion that the proposed estimator outperforms as compared to all other estimators taken into account in this study.

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Estimation of population mean under probability proportional to size sampling with and without measurement errors

Cited by 4 publications

References 18 publications

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

Calibration Estimation of Population Proportion in Probability Proportional to Size Sampling in the Presence of Non-Response

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

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