Joint Asymptotics for Estimating the Fractal Indices of Bivariate Gaussian Processes
Statistics Theory
2017-07-25 v2 Statistics Theory
Abstract
Multivariate (or vector-valued) processes are important for modeling multiple variables. The fractal indices of the components of the underlying multivariate process play a key role in characterizing the dependence structures and statistical properties of the multivariate process. In this paper, under the infill asymptotics framework, we establish joint asymptotic results for the increment-based estimators of bivariate fractal indices. Our main results quantitatively describe the effect of the cross-dependence structure on the performance of the estimators.
Keywords
Cite
@article{arxiv.1609.03470,
title = {Joint Asymptotics for Estimating the Fractal Indices of Bivariate Gaussian Processes},
author = {Yuzhen Zhou and Yimin Xiao},
journal= {arXiv preprint arXiv:1609.03470},
year = {2017}
}