Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights
Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In thi...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2020/8090381 |
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| author | Nelson Kiprono Bii Christopher Ouma Onyango John Odhiambo |
| author_facet | Nelson Kiprono Bii Christopher Ouma Onyango John Odhiambo |
| author_sort | Nelson Kiprono Bii |
| collection | DOAJ |
| description | Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys. |
| format | Article |
| id | doaj-art-0d1d6c194ff745c3babe2dfedcf403e5 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-0d1d6c194ff745c3babe2dfedcf403e52025-02-03T06:05:15ZengWileyJournal of Probability and Statistics1687-952X1687-95382020-01-01202010.1155/2020/80903818090381Estimation of a Finite Population Mean under Random Nonresponse Using Kernel WeightsNelson Kiprono Bii0Christopher Ouma Onyango1John Odhiambo2Institute of Mathematical Sciences, Strathmore University, P.O. Box 59857-00200, Nairobi, KenyaDepartment of Statistics, Kenyatta University, P.O. Box 43844-00200, Nairobi, KenyaInstitute of Mathematical Sciences, Strathmore University, P.O. Box 59857-00200, Nairobi, KenyaNonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.http://dx.doi.org/10.1155/2020/8090381 |
| spellingShingle | Nelson Kiprono Bii Christopher Ouma Onyango John Odhiambo Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights Journal of Probability and Statistics |
| title | Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights |
| title_full | Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights |
| title_fullStr | Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights |
| title_full_unstemmed | Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights |
| title_short | Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights |
| title_sort | estimation of a finite population mean under random nonresponse using kernel weights |
| url | http://dx.doi.org/10.1155/2020/8090381 |
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