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Noncentral moderate deviations for time-changed Lévy processes with inverse of stable subordinators
Volume 12, Issue 2 (2025), pp. 203–224
Antonella Iuliano ORCID icon link to view author Antonella Iuliano details   Claudio Macci ORCID icon link to view author Claudio Macci details   Alessandra Meoli ORCID icon link to view author Alessandra Meoli details  

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https://doi.org/10.15559/24-VMSTA269
Pub. online: 31 December 2024      Type: Research Article      Open accessOpen Access

Received
7 August 2024
Revised
16 December 2024
Accepted
17 December 2024
Published
31 December 2024

Abstract

This paper presents some extensions of recent noncentral moderate deviation results. In the first part, the results in [Statist. Probab. Lett. 185, Paper No. 109424, 8 pp. (2022)] are generalized by considering a general Lévy process $\{S(t):t\ge 0\}$ instead of a compound Poisson process. In the second part, it is assumed that $\{S(t):t\ge 0\}$ has bounded variation and is not a subordinator; thus $\{S(t):t\ge 0\}$ can be seen as the difference of two independent nonnull subordinators. In this way, the results in [Mod. Stoch. Theory Appl. 11, 43–61] for Skellam processes are generalized.

References

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Copyright
© 2025 The Author(s). Published by VTeX
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Open access article under the CC BY license.

Keywords
Large deviations weak convergence Mittag-Leffler function tempered stable subordinators

MSC2020
60F10 60F05 60G22 33E12

Funding
A.I. acknowledges the support from MUR-PRIN 2022 PNRR (project P2022XSF5H “Stochastic Models in Biomathematics and Applications”) and INdAM-GNCS.
C.M. acknowledges the support from MUR Excellence Department Project awarded to the Department of Mathematics, University of Rome Tor Vergata (CUP E83C23000330006), from University of Rome Tor Vergata (project “Asymptotic Properties in Probability” (CUP E83C22001780005)) and INdAM-GNAMPA.
A.M. acknowledges the support from MUR-PRIN 2022 (project 2022XZSAFN “Anomalous Phenomena on Regular and Irregular Domains: Approximating Complexity for the Applied Sciences”), from MUR-PRIN 2022 PNRR (project P2022XSF5H “Stochastic Models in Biomathematics and Applications”) and INdAM-GNCS.

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