Robust regression estimation based on data partitioning
- Authors
- Lee, Dong-Hee; Park, Yousung
- Issue Date
- Jun-2007
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- computation problem; data partitioning; efficiency; high breakdown point; outlier detection; performance in large sample
- Citation
- JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.36, no.2, pp.299 - 320
- Indexed
- SCIE
KCI
- Journal Title
- JOURNAL OF THE KOREAN STATISTICAL SOCIETY
- Volume
- 36
- Number
- 2
- Start Page
- 299
- End Page
- 320
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/125770
- ISSN
- 1226-3192
- Abstract
- We introduce a high breakdown point estimator referred to as data partitioning robust regression estimator (DPR). Since the DPR is obtained by partitioning observations into a finite number of subsets, it has no computational problem unlike the previous robust regression estimators. Empirical and extensive simulation studies show that the DPR is superior to the previous robust estimators. This is much so in large samples.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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