Published 2001
| Version v1
Miscellaneous
Identifying multiple outliers in linear regression: robust fit and clustering approach
Creators
- 1. Universiti Teknologi Malaysia, Skudai (Malaysia). Faculty of Science
- 2. Universiti Teknologi Malaysia, Skudai (Malaysia). Faculty of Geoinformation Science and Engineering
Description
This research provides a clustering based approach for determining potential candidates for outliers. This is modification of the method proposed by Serbert et. al (1988). It is based on using the single linkage clustering algorithm to group the standardized predicted and residual values of data set fit by least trimmed of squares (LTS). (Author)
Additional details
Publishing Information
- Publisher
- Confederation of Scientific and Technological Associations in Malaysia COSTAM
- Imprint Place
- Petaling Jaya (Malaysia)
- Imprint Title
- Proceedings of the Malaysian Science and Technology Congress 2000: Symposium C, Vol. VI
- Imprint Pagination
- 400 p.
- Journal Page Range
- p. 226-233
Conference
- Title
- Research and Development in Science and Technology for the New Era - Symposium C
- Acronym
- Malaysian Science and Technology Congress 2000
- Dates
- 7-9 Nov 2000
- Place
- Genting Highlands (Malaysia)
INIS
- Country of Publication
- Malaysia
- Country of Input or Organization
- Malaysia
- INIS RN
- 34031908
- Subject category
- S99: GENERAL AND MISCELLANEOUS;
- Resource subtype / Literary indicator
- Conference, Non-conventional Literature
- Descriptors DEI
- ALGORITHMS; CLUSTER MODEL; LEAST SQUARE FIT; MODIFICATIONS; REGRESSION ANALYSIS
- Descriptors DEC
- MATHEMATICAL LOGIC; MATHEMATICAL MODELS; MATHEMATICS; MAXIMUM-LIKELIHOOD FIT; NUCLEAR MODELS; NUMERICAL SOLUTION; STATISTICS