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A chain regression exponential type imputation methodfor mean estimation in the presence of missing data |
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| รหัสดีโอไอ | |
| Creator | 1. Kanisa Chodjuntug 2. Nuanpan Lawson |
| Title | A chain regression exponential type imputation methodfor mean estimation in the presence of missing data |
| Publisher | Research and Development Office, Prince of Songkla University |
| Publication Year | 2565 |
| Journal Title | Songklanakarin Journal of Science an Technology (SJST) |
| Journal Vol. | 44 |
| Journal No. | 4 |
| Page no. | 1109-1118 |
| Keyword | imputation, regression estimator, bias, mean square error, missing data |
| URL Website | https://rdo.psu.ac.th/sjst/index.php |
| ISSN | 0125-3395 |
| Abstract | Imputation methods deal with item nonresponse to solve the missing data problem. A new imputation method andcorresponding point estimators for population mean have been proposed under two situations: using the response rate and theconstant that gives the minimum mean square error for the estimator. The biases and mean square errors of the proposedestimators are derived. The performance of this method is compared with some existing methods via simulations and anapplication to fine particulate matter data. The results show that the proposed estimator, which uses the optimum value of aconstant, performs the best. It performs the second best when using the response rate in the estimator, which is free of knownparameters. The estimated fine particulate matter in Kanchana Phisek Road in Bangkok using the best method is equivalent to42.22 micrograms per cubic meter with a mean square error of 0.34 micrograms per cubic meter squared. |
