A comparison study of multiple linear quantile regression using non-crossing constraints
- Authors
- Bang, Sungwan; Shin, Seung Jun
- Issue Date
- 8월-2016
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- multiple linear quantile regression; non-crossing; linear programming
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.29, no.5, pp.773 - 786
- Indexed
- KCI
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 29
- Number
- 5
- Start Page
- 773
- End Page
- 786
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/87899
- DOI
- 10.5351/KJAS.2016.29.5.773
- ISSN
- 1225-066X
- Abstract
- Multiple quantile regression that simultaneously estimate several conditional quantiles of response given covariates can provide a comprehensive information about the relationship between the response and covariates. Some quantile estimates can cross if conditional quantiles are separately estimated; however, this violates the definition of the quantile. To tackle this issue, multiple quantile regression with non-crossing constraints have been developed. In this paper, we carry out a comparison study on several popular methods for non-crossing multiple linear quantile regression to provide practical guidance on its application.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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