Why is linear quantile regression empirically successful: A possible explanation
© 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...
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th-cmuir.6653943832-410902017-09-28T04:15:28Z Why is linear quantile regression empirically successful: A possible explanation Nguyen H. Kreinovich V. Kosheleva O. Sriboonchitta S. © 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. 2017-09-28T04:15:28Z 2017-09-28T04:15:28Z 2017-01-01 Book Series 1860949X 2-s2.0-85012066355 10.1007/978-3-319-51052-1_11 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012066355&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41090 |
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© 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. |
| format |
Book Series |
| author |
Nguyen H. Kreinovich V. Kosheleva O. Sriboonchitta S. |
| spellingShingle |
Nguyen H. Kreinovich V. Kosheleva O. Sriboonchitta S. Why is linear quantile regression empirically successful: A possible explanation |
| author_facet |
Nguyen H. Kreinovich V. Kosheleva O. Sriboonchitta S. |
| author_sort |
Nguyen H. |
| title |
Why is linear quantile regression empirically successful: A possible explanation |
| title_short |
Why is linear quantile regression empirically successful: A possible explanation |
| title_full |
Why is linear quantile regression empirically successful: A possible explanation |
| title_fullStr |
Why is linear quantile regression empirically successful: A possible explanation |
| title_full_unstemmed |
Why is linear quantile regression empirically successful: A possible explanation |
| title_sort |
why is linear quantile regression empirically successful: a possible explanation |
| publishDate |
2017 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85012066355&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41090 |
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