Open AccessBayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process
Author(s) -
Judith Rousseau,
Nicolas Chopin,
Brunero Liseo
Publication year - 2012
Publication title -
hal (le centre pour la communication scientifique directe)
Language(s) - English
DOI - 10.1214/11-aos955supp
Subject(s) - gaussian process , nonparametric statistics , bayesian probability , estimation , process (computing) , density estimation , dirichlet process , gaussian , statistical physics , computer science , econometrics , mathematics , statistics , artificial intelligence , pattern recognition (psychology) , physics , chemistry , engineering , computational chemistry , systems engineering , operating system , estimator
International audienceA stationary Gaussian process is said to be long-range dependent (resp. anti-persistent) if its spectral density $f(\lambda)$ can be written as $f(\lambda)=|\lambda|^{-2d}g(|\lambda|)$, where $