Joseph, Ajin George ; Bhatnagar, Shalabh (2015) A Stochastic Approximation Algorithm for Quantile Estimation In: Proceedings of 22nd International Conference on Neural Information Processing (ICONIP), Nov.9-12, 2015, Istanbul, Turkey.
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Official URL: http://doi.org/10.1007/978-3-319-26535-3_36
Related URL: http://dx.doi.org/10.1007/978-3-319-26535-3_36
Abstract
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 or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to Springer Nature. |
| Keywords: | Probability Density Function; Stochastic Approximation; Latin Hypercube Sampling; Global Asymptotic Stability; Cauchy Distribution. |
| ID Code: | 116653 |
| Deposited On: | 12 Apr 2021 07:18 |
| Last Modified: | 12 Apr 2021 07:18 |
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