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| Field |
Value |
| Title: | Computing Least Trimmed Squares Regression with the Forward Search |
| Authors: | Atkinson A.C;鄭宗記
Cheng,Tsung-Chi |
| Contributors: | 統計系 |
| Date: | 1999.11 |
| Issue Date: | 2014-11-06 18:23:10 (UTC+8) |
| Abstract: | Least trimmed squares (LTS) provides a parametric family of high breakdown estimators in regression with better asymptotic properties than least median of squares (LMS) estimators. We adapt the forward search algorithm of Atkinson (1994) to LTS and provide methods for determining the amount of data to be trimmed. We examine the efficiency of different trimming proportions by simulation and demonstrate the increasing efficiency of parameter estimation as larger proportions of data are fitted using the LTS criterion. Some standard data examples are analysed. One shows that LTS provides more stable solutions than LMS. |
| Relation: | Statistics and Computing9(4),251-263 |
| Data Type: | article |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 統計系 | en_US |
| dc.creator (Authors) | Atkinson A.C;鄭宗記 | en_US |
| dc.creator (Authors) | Cheng,Tsung-Chi | en_US |
| dc.date (Date) | 1999.11 | en_US |
| dc.date.accessioned | 2014-11-06 18:23:10 (UTC+8) | - |
| dc.date.available | 2014-11-06 18:23:10 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2014-11-06 18:23:10 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/71197 | - |
| dc.description.abstract (Abstract) | Least trimmed squares (LTS) provides a parametric family of high breakdown estimators in regression with better asymptotic properties than least median of squares (LMS) estimators. We adapt the forward search algorithm of Atkinson (1994) to LTS and provide methods for determining the amount of data to be trimmed. We examine the efficiency of different trimming proportions by simulation and demonstrate the increasing efficiency of parameter estimation as larger proportions of data are fitted using the LTS criterion. Some standard data examples are analysed. One shows that LTS provides more stable solutions than LMS. | en_US |
| dc.format.extent | 412310 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (Relation) | Statistics and Computing9(4),251-263 | en_US |
| dc.title (Title) | Computing Least Trimmed Squares Regression with the Forward Search | en_US |
| dc.type (Data Type) | article | en |
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