A Modified Regression Estimator for Single Phase Sampling in the Presence of Observational Errors

Salim, Nujayma M. A.; Onyango, Christopher O.

doi:10.4236/ojs.2022.122012

Cited by 2 publications

(4 citation statements)

References 13 publications

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“…Table 3 presents the results of the biases, MSEs and PREs of the reviewed estimators and that of proposed estimators using simulated data from population 3 defined in Table 1. The results shows that all the proposed estimators under imputation schemes has minimum MSEs and higher PREs compared to all the existing estimators cons9idered in this study with the exception Salim and Onyango [29] estimator ( so t ) and Maneer et al [28] estimator (   1 m t ) which is a little higher than the proposed estimator ( ** 1 T ). Also, the proposed estimator ( * 4 T ) under call-back has minimum MSEs and higher PREs than most of the estimators under study with the exception Salim and Onyango [29] so t , Maneer et al [28] estimator (   Table 4 presents the results of the biases, MSEs and PREs of the reviewed estimators and that of proposed estimators using simulated data from population 5 defined in Table 1.…”

Section:    mentioning

confidence: 55%

“…The results shows that all the proposed estimators under imputation schemes has minimum MSEs and higher PREs compared to all the existing estimators considered in this study. Also, the proposed estimator ( * 1 T ) under call-back has minimum MSEs and higher PREs than most of the estimators under study with the exception of Salim and Onyango [29] so t , Maneer et al [28] estimators (  …”

Section:    mentioning

confidence: 86%

“…T ) under call-back has minimum MSEs and higher PREs than most of the estimators under study with the exception of Salim and Onyango [29] so t , Muneer et al [28] estimators  …”

Section:    mentioning

confidence: 87%

“…Salim and Onyango [29] proposed a modified estimator for single phase sampling in the presence of observational errors as in ( 16)…”

Section: mentioning

confidence: 99%

See 3 more Smart Citations

Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error

Audu,

Usman,

Mohammad

et al. 2023

AJPAS

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The use of estimators in statistics, quality assurance, and survey methodology can never be over flogged just as the use of sampling. Two of the major challenges of statisticians or surveyors due encounter at the course of data collection in the field of medical and social sciences is non-response and measurement errors. This poses serious problem during data compilation, computation and estimation stages. In this paper, a robust-based classes of estimators are proposed in the presence of non-response and measurement errors through the use of imputation scheme incorporated with measurement errors parameters. The properties of the proposed estimators (Biases & MSES) were derived up to the second degree approximation using Taylors’s series approach. The conditions for the efficiencies of the proposed estimators over the existing estimators was also considered and established in this research. The empirical study conducted using simulated data from normal distribution, exponential distribution, chi-square distribution, uniform distribution, gamma distribution and poison distribution revealed that the modified classes of estimators of the proposed imputation schemes are more efficient and satisfactory than the compared existing estimators. Thus, the proposed modified classes of estimators under imputation scheme were recommended for use in the real life situation especially in the presence of non-response and measurement errors during data analysis and estimation stages.

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Section:    mentioning

confidence: 55%

Section:    mentioning

confidence: 86%

“…T ) under call-back has minimum MSEs and higher PREs than most of the estimators under study with the exception of Salim and Onyango [29] so t , Muneer et al [28] estimators  …”

Section:    mentioning

confidence: 87%

“…Salim and Onyango [29] proposed a modified estimator for single phase sampling in the presence of observational errors as in ( 16)…”

Section: mentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error

Audu,

Usman,

Mohammad

et al. 2023

AJPAS

View full text Add to dashboard Cite

show abstract

Performance Evalluation of Imputation-Based Estimators for Non-Response and Measurement Error Challenges

Joseph,

Shehu

2024

FJS

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This study aims to develop a robust class of estimators designed to address non-response and measurement erros, which frequently complicate data collection in mdeical and social science research. By employing call-back and imputation schemes, the proposed estimators enhance efficiency and accuracy. We derived properties such as bias and mean squared error using Taylor's series explansion and testes their consistenct. An empirical study with simulated data rfom vaious distributions revealed that the proposed estimators outperform existing ones. Thus, these modified classes are recommended for practical application in data analysis, especially in the presence of non-reponse and measurement errors.

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A Modified Regression Estimator for Single Phase Sampling in the Presence of Observational Errors

Cited by 2 publications

References 13 publications

Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error

Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error

Performance Evalluation of Imputation-Based Estimators for Non-Response and Measurement Error Challenges

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