Nonparametric estimation for compound Poisson process via variational analysis on measures

Lindo, A. , Zuyev, S. and Sagitov, S. (2018) Nonparametric estimation for compound Poisson process via variational analysis on measures. Statistics and Computing, 28(3), pp. 563-577. (doi: 10.1007/s11222-017-9748-4)

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The paper develops new methods of nonparametric estimation of a compound Poisson process. Our key estimator for the compounding (jump) measure is based on series decomposition of functionals of a measure and relies on the steepest descent technique. Our simulation studies for various examples of such measures demonstrate flexibility of our methods. They are particularly suited for discrete jump distributions, not necessarily concentrated on a grid nor on the positive or negative semi-axis. Our estimators also applicable for continuous jump distributions with an additional smoothing step.

Item Type:Articles
Additional Information:This work was supported by Swedish Research Council Grant No. 11254331.
Glasgow Author(s) Enlighten ID:Lindo, Dr Alexey
Authors: Lindo, A., Zuyev, S., and Sagitov, S.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Statistics and Computing
ISSN (Online):1573-1375
Published Online:19 April 2017
Copyright Holders:Copyright © 2017 The Authors
First Published:First published in Statistics and Computing 28(3):563-577
Publisher Policy:Reproduced under a Creative Commons license

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