Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorOlga Koshelevaen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.description.abstract© Springer International Publishing AG 2017. Many quantities describing the physical world are related to each other. As a result, often, when we know the values of certain quantities x1,…, xn, we can reasonably well predict the value of some other quantity y. In many application, in addition to the resulting estimate for y, it is also desirable to predict how accurate is this approximate estimate, i.e., what is the probability distribution of different possible values y. It turns out that in many cases, the quantiles of this distribution linearly depend on the values x1,…, xn. In this paper, we provide a possible theoretical explanation for this somewhat surprising empirical success of such linear quantile regression.en_US
dc.subjectComputer Scienceen_US
dc.titleWhy is linear quantile regression empirically successful: A possible explanationen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume683en_US Mexico State University Las Crucesen_US Mai Universityen_US of Texas at El Pasoen_US
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.

Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.