Kernel conditional quantile estimation via reduction revisited

Quadrianto, Novi, Kersting, Kristian, Reid, Mark D, Caetano, Tiberio S and Buntine, Wray L (2009) Kernel conditional quantile estimation via reduction revisited. Published in: Wang, Wei, Kargupta, Hillol, Ranka, Sanjay, Yu, Philip S and Wu, Xindong, (eds.) Proceedings of the 9th IEEE International Conference on Data Mining; Miami, Florida; 6-9 December 2009. 938-943. Institute of Electrical and Electronics Engineers, Los Alamitos, California. ISSN 1550-4786 ISBN 9781424452422

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Abstract

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches."

Item Type: Conference Proceedings
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > Q Science (General)
Depositing User: Novi Quadrianto
Date Deposited: 24 Feb 2014 13:20
Last Modified: 16 Jun 2017 15:48
URI: http://sro.sussex.ac.uk/id/eprint/47607

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