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725 results on '"Samorodnitsky, Gennady"'

164. A class of shot noise models for financial applications

Author

Samorodnitsky, Gennady, Bickel, P., editor, Diggle, P., editor, Fienberg, S., editor, Krickeberg, K., editor, Olkin, I., editor, Wermuth, N., editor, Zeger, S., editor, Heyde, C. C., editor, Prohorov, Yu V., editor, Pyke, Ronald, editor, and Rachev, S. T., editor

Published
1996
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191. Extreme Bandits Using Robust Statistics

Author

Bhatt, Sujay, Li, Ping, and Samorodnitsky, Gennady

Abstract

Motivated by situations where the extreme values – as opposed to expected values in the classical stochastic multi-armed bandit (MAB) setting – are of interest, we propose a distribution-free algorithm for $\textit {extreme bandits}$ and characterize its statistical properties. The proposed novel algorithm is index based, where the index is fashioned in a non-parametric way using combinatorics and robust statistics. For distributions having “exponential-like tails” and “polynomial-like tails”, we establish the following results: (i) the proposed algorithm is consistent, i.e., the index corresponding to the best arm will have the largest value asymptotically; (ii) the proposed algorithm achieves vanishing extremal regret under weaker conditions than the existing algorithms. Numerical experiments on the common class of distributions considered in the literature on extreme bandits highlight the superior finite-sample performance of the proposed algorithm compared to the state of the art.

Published
2023
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195. Distance covariance for discretized stochastic processes

Author

Dehling, Herold G., Matsui, Muneya, Mikosch, Thomas Valentin, Samorodnitsky, Gennady, Tafakori, Laleh, Dehling, Herold G., Matsui, Muneya, Mikosch, Thomas Valentin, Samorodnitsky, Gennady, and Tafakori, Laleh

Abstract

Given an i.i.d. sequence of pairs of stochastic processes on the unit interval we construct a measure of independence for the components of the pairs. We define distance covariance and distance correlation based on approximations of the component processes at finitely many discretization points. Assuming that the mesh of the discretization converges to zero as a suitable function of the sample size, we show that the sample distance covariance and correlation converge to limits which are zero if and only if the component processes are independent. To construct a test for independence of the discretized component processes, we show consistency of the bootstrap for the corresponding sample distance covariance/correlation.

Published
2020

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