Addressing non-response and measurement errors in time-scaled surveys
Abstract Measurement and non-response errors significantly affect the accuracy of estimates. Measurement errors, from inaccurate data collection, distort variable relationships and bias results, while non-response errors, from missing data, lead to unrepresentative samples, especially when systemati...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06676-0 |
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| author | Poonam Singh Pooja Maurya Prayas Sharma |
| author_facet | Poonam Singh Pooja Maurya Prayas Sharma |
| author_sort | Poonam Singh |
| collection | DOAJ |
| description | Abstract Measurement and non-response errors significantly affect the accuracy of estimates. Measurement errors, from inaccurate data collection, distort variable relationships and bias results, while non-response errors, from missing data, lead to unrepresentative samples, especially when systematic. Both increase variability, reduce precision, and compromise conclusions, risking flawed decisions.To tackle these challenges, we have developed a generalized class of exponential estimators to enhance the accuracy of population mean estimation in time-scaled surveys. We analyzed the impact of measurement and non-response errors on accuracy by examining two scenarios: one where non-response affects only the study variable and another where it impacts both the study and auxiliary variables, with measurement error accounted for in both cases. We derive expressions for the bias and mean square error of the proposed estimator, considering the effects of measurement error and non-response, up to the first-order approximation. For time-scaled surveys, we compare its performance with several existing estimators. Extensive simulation studies demonstrate that the proposed estimator achieves greater efficiency in addressing these errors compared to current methods. |
| format | Article |
| id | doaj-art-0a7c0125c5ca4341b2c802ba6cf70990 |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-0a7c0125c5ca4341b2c802ba6cf709902025-08-20T02:17:56ZengSpringerDiscover Applied Sciences3004-92612025-04-017512710.1007/s42452-025-06676-0Addressing non-response and measurement errors in time-scaled surveysPoonam Singh0Pooja Maurya1Prayas Sharma2Department of Statistics, Banaras Hindu UniversityDepartment of Statistics, Banaras Hindu UniversityDepartment of Statistics, Babasaheb Bhimrao Ambedkar UniversityAbstract Measurement and non-response errors significantly affect the accuracy of estimates. Measurement errors, from inaccurate data collection, distort variable relationships and bias results, while non-response errors, from missing data, lead to unrepresentative samples, especially when systematic. Both increase variability, reduce precision, and compromise conclusions, risking flawed decisions.To tackle these challenges, we have developed a generalized class of exponential estimators to enhance the accuracy of population mean estimation in time-scaled surveys. We analyzed the impact of measurement and non-response errors on accuracy by examining two scenarios: one where non-response affects only the study variable and another where it impacts both the study and auxiliary variables, with measurement error accounted for in both cases. We derive expressions for the bias and mean square error of the proposed estimator, considering the effects of measurement error and non-response, up to the first-order approximation. For time-scaled surveys, we compare its performance with several existing estimators. Extensive simulation studies demonstrate that the proposed estimator achieves greater efficiency in addressing these errors compared to current methods.https://doi.org/10.1007/s42452-025-06676-0Auxiliary variableSimple random sampling without replacement (SRSWOR)Exponentially weighted moving average (EWMA)Non-responseMeasurement errorBias |
| spellingShingle | Poonam Singh Pooja Maurya Prayas Sharma Addressing non-response and measurement errors in time-scaled surveys Discover Applied Sciences Auxiliary variable Simple random sampling without replacement (SRSWOR) Exponentially weighted moving average (EWMA) Non-response Measurement error Bias |
| title | Addressing non-response and measurement errors in time-scaled surveys |
| title_full | Addressing non-response and measurement errors in time-scaled surveys |
| title_fullStr | Addressing non-response and measurement errors in time-scaled surveys |
| title_full_unstemmed | Addressing non-response and measurement errors in time-scaled surveys |
| title_short | Addressing non-response and measurement errors in time-scaled surveys |
| title_sort | addressing non response and measurement errors in time scaled surveys |
| topic | Auxiliary variable Simple random sampling without replacement (SRSWOR) Exponentially weighted moving average (EWMA) Non-response Measurement error Bias |
| url | https://doi.org/10.1007/s42452-025-06676-0 |
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