Bayesian Inference on the Memory Parameter for Gamma-Modulated Regression Models

Plinio Andrade, Laura Rifo, Soledad Torres, Francisco Torres-Avilés

In this work, we propose a Bayesian methodology to make inferences for the memory parameter and other characteristics under non-standard assumptions for a class of stochastic processes. This class generalizes the Gamma-modulated process, with trajectories that exhibit long memory behavior, as well as decreasing variability as time increases. Different values of the memory parameter influence the speed of this decrease, making this heteroscedastic model very flexible. Its properties are used to implement an approximate Bayesian computation and MCMC scheme to obtain posterior estimates. We test and validate our method through simulations and real data from the big earthquake that occurred in 2010 in Chile.

The whole paper is available here.

NeuroCineMat
Featuring this week:
Newsletter

Stay informed on our latest news!



Previous issues

Podcast A Matemática do Cérebro
Podcast A Matemática do Cérebro
NeuroMat Brachial Plexus Injury Initiative
Logo of the NeuroMat Brachial Plexus Injury Initiative
Neuroscience Experiments System
Logo of the Neuroscience Experiments System
NeuroMat Parkinson Network
Logo of the NeuroMat Parkinson Network
NeuroMat's scientific-dissemination blog
Logo of the NeuroMat's scientific-dissemination blog