| dc.creator (作者) | Atkinson, Anthony C.;鄭宗記 | en_US |
| dc.creator (作者) | Atkinson, Anthony C.;Cheng, Tsung-Chi | - |
| dc.date (日期) | 2000-06 | en_US |
| dc.date.accessioned | 19-Dec-2008 14:52:10 (UTC+8) | - |
| dc.date.available | 19-Dec-2008 14:52:10 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-Dec-2008 14:52:10 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/18164 | - |
| dc.description.abstract (摘要) | In this paper, we use recently developed methods of very robust regression to extend missing value techniques to data with several outliers. Simulation experiments reveal that additional outliers may be imputed if one ignores the outliers already in the data. The combination of the forward search algorithm for high breakdown point estimators and the EM algorithm or multiple imputation for missing values can avoid problems of this kind. Some multiple deletion diagnostics for linear regression with incomplete data are also discussed. | - |
| dc.format | application/ | en_US |
| dc.language | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Computational Statistics and Data Analysis 33(4),361-380 | en_US |
| dc.subject (關鍵詞) | EM algorithm;Forward search algorithm;High breakdown point;Least trimmed squares;Missing values;Multiple imputation;Regression diagnostics;Stalactite plot | - |
| dc.title (題名) | On Robust Linear Regression with Incomplete Data | en_US |
| dc.type (資料類型) | article | en |
