| Evaluation of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Missing-Data Mechanisms | |
|---|---|
| 學年 | 99 |
| 學期 | 2 |
| 出版（發表）日期 | 2011-06-01 |
| 作品名稱 | Evaluation of Multiple Imputation for Longitudinal Ordinal Data under MCAR and MAR Missing-Data Mechanisms |
| 作品名稱（其他語言） | |
| 著者 | Tuan, Li-Wen; Chen, Yi-Ju; Li, Pai-Ling; Lin, Kuo-Chin |
| 單位 | 淡江大學統計學系 |
| 出版者 | Toroku: ICIC International |
| 著錄名稱、卷期、頁數 | ICIC Express Letters 5(6), pp.1833-1838 |
| 摘要 | Multiple imputation can be used to solve the problem of missing data that is a common occurrence in longitudinal studies. An imputation strategy proposed by Demirtas and Hedeker (Statistics in Medicine 2008; 27, 4086-4093) is to deal with incomplete longitudinal ordinal data, which converts discrete outcomes to continuous outcomes by generating normal values, employs multiple method based on normality, and reconverts to binary scale as well as ordinal one. The performance of multiple imputation in terms of standardized bias, root-mean-squared error and coverage percentage under missing completely at random (MCAR) and missing at random (MAR) was discussed by various configurations. The simulated results indicated this mutation strategy is suitable for most of incomplete data under these two missing-data mechanisms. |
| 關鍵字 | MAR; MCAR; Multiple imputation; Ordinal scale |
| 語言 | en |
| ISSN | 1881-803X |
| 期刊性質 | 國外 |
| 收錄於 | EI |
| 產學合作 | |
| 通訊作者 | Chen, Yi-Ju |
| 審稿制度 | 是 |
| 國別 | JPN |
| 公開徵稿 | |
| 出版型式 | 紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58475 ) |
| SDGS | 良好健康和福祉,優質教育 |
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