Gaussian Approximations for Non-stationary Multiple Time Series

Abstract

We obtain an invariance principle for non-stationary vector-valued stochastic processes. It is shown that, under mild conditions, the partial sums of non-stationary processes can be approximated on a richer probability space by sums of independent Gaussian random vectors with nearly optimal bounds. The latter Gaussian approximation result has a wide range of applications in the study of multiple non-stationary time series.

Cite this paper

@inproceedings{Wu2011GaussianAF, title={Gaussian Approximations for Non-stationary Multiple Time Series}, author={Wei Biao Wu and Zhou Zhou}, year={2011} }