On De la Peña Type Inequalities for Point Processes
Open Access
- 17 June 2022
- journal article
- research article
- Published by MDPI AG in Mathematics
- Vol. 10 (12) , 2114
Abstract
There has been a renewed interest in exponential concentration inequalities for stochastic processes in probability and statistics over the last three decades. De la Peña established a nice exponential inequality for a discrete time locally square integrable martingale. In this paper, we obtain de la Peña’s inequalities for a stochastic integral of multivariate point processes. The proof is primarily based on Doléans–Dade exponential formula and the optional stopping theorem. As an application, we obtain an exponential inequality for block counting process in coalescent.
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Funding Information
- National Key Research and Development Program of China (2018YFA0703900, ZR2019ZD41)
- Natural Science Foundation of Shandong Province (2018YFA0703900, ZR2019ZD41)
This publication has 19 references indexed in Scilit:
- Hoeffding’s inequality for supermartingalesStochastic Processes and their Applications, 2012
- Exponential inequalities for self-normalized martingales with applicationsThe Annals of Applied Probability, 2008
- Self-normalized processes: exponential inequalities, moment bounds and iterated logarithm lawsThe Annals of Probability, 2004
- On Bernstein-type inequalities for martingalesStochastic Processes and their Applications, 2001
- The general coalescent with asynchronous mergers of ancestral linesJournal of Applied Probability, 1999
- A General Class of Exponential Inequalities for Martingales and RatiosThe Annals of Probability, 1999
- Deviation inequalities for continuous martingalesStochastic Processes and their Applications, 1996
- Exponential Inequalities for Martingales, with Application to Maximum Likelihood Estimation for Counting ProcessesThe Annals of Statistics, 1995
- On Tail Probabilities for MartingalesThe Annals of Probability, 1975
- Probability Inequalities for Sums of Bounded Random VariablesJournal of the American Statistical Association, 1963