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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Abstract
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 |
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| Additional Information: | This work was supported by Swedish Research Council Grant No. 11254331. |
| Status: | Published |
| Refereed: | Yes |
| 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 |
| Publisher: | Springer |
| ISSN: | 0960-3174 |
| 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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