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A Stochastic Approximation Algorithm for Quantile Estimation

Joseph, Ajin George and Bhatnagar, Shalabh (2016) A Stochastic Approximation Algorithm for Quantile Estimation. In: 22nd International Conference on Neural Information Processing (ICONIP), NOV 09-12, 2015, Istanbul, TURKEY, pp. 311-319.

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Official URL: http://dx.doi.org/10.1007/978-3-319-26535-3_36


In this paper, we present two new stochastic approximation algorithms for the problem of quantile estimation. The algorithms uses the characterization of the quantile provided in terms of an optimization problem in 1]. The algorithms take the shape of a stochastic gradient descent which minimizes the optimization problem. Asymptotic convergence of the algorithms to the true quantile is proven using the ODE method. The theoretical results are also supplemented through empirical evidence. The algorithms are shown to provide significant improvement in terms of memory requirement and accuracy.

Item Type: Conference Proceedings
Series.: Lecture Notes in Computer Science
Additional Information: Copy right for this article belongs to the SPRINGER INT PUBLISHING AG, GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 06 Apr 2016 06:04
Last Modified: 11 Oct 2018 12:15
URI: http://eprints.iisc.ac.in/id/eprint/53640

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