Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/41090
Title: Why is linear quantile regression empirically successful: A possible explanation
Authors: Nguyen H.
Kreinovich V.
Kosheleva O.
Sriboonchitta S.
Issue Date: 1-Jan-2017
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 x 1 ,…, x n , 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 x 1 ,…, x n . In this paper, we provide a possible theoretical explanation for this somewhat surprising empirical success of such linear quantile regression.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012066355&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/41090
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

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