Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition

03/09/2021
by   Anna Bonnet, et al.
0

In this paper, we present a maximum likelihood method for estimating the parameters of a univariate Hawkes process with self-excitation or inhibition. Our work generalizes techniques and results that were restricted to the self-exciting scenario. The proposed estimator is implemented for the classical exponential kernel and we show that, in the inhibition context, our procedure provides more accurate estimations than current alternative approaches.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/13/2023

Maximum Likelihood Estimation for Maximal Distribution under Sublinear Expectation

Maximum likelihood estimation is a common method of estimating the param...
research
02/23/2021

EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss

Maximum likelihood estimation is widely used in training Energy-based mo...
research
03/26/2022

An adaptive residual sub-sampling algorithm for kernel interpolation based on maximum likelihood estimations

In this paper we propose an enhanced version of the residual sub-samplin...
research
03/29/2019

Estimation of cell lineage trees by maximum-likelihood phylogenetics

CRISPR technology has enabled large-scale cell lineage tracing for compl...
research
05/28/2020

Hawkes process and Edgeworth expansion with application to maximum likelihood estimator

The Hawks process is a point process with a self-exciting property. It h...
research
08/28/2021

Maximum Likelihood Estimation of Diffusions by Continuous Time Markov Chain

In this paper we present a novel method for estimating the parameters of...
research
05/11/2018

Essential formulae for restricted maximum likelihood and its derivatives associated with the linear mixed models

The restricted maximum likelihood method enhances popularity of maximum ...

Please sign up or login with your details

Forgot password? Click here to reset