About monotone regression quantiles

Sandrine Casanova and Christine Thomas-Agnan

Statistics & Probability Letters, 2000, vol. 48, issue 1, 101-104

Abstract: The aim of this paper is to prove the equivalence between the regression quantile under monotonicity constraint and the Min-Max formula introduced by Casady and Cryer (1976, Ann. Math. Statist. 4 (3), 532-541). The proof of this result uses an original probability density which does not appear in classical books.

Keywords: Monotonicity; Regression; quantile; Min-Max; formula (search for similar items in EconPapers)
Date: 2000
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(99)00200-X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:48:y:2000:i:1:p:101-104

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().