Adaptive covariance estimation of non-stationary processes and its application to infer dynamic connectivity from fMRI.

ABSTRACT: Time-varying covariance is an important metric to measure the statistical dependence between non-stationary biological processes. Time-varying covariance is conventionally estimated from short-time data segments within a window having a certain bandwidth, but it is difficult to choose an appropriate bandwidth to estimate covariance with different degrees of non-stationarity. This paper introduces a local polynomial regression (LPR) method to estimate time-varying covariance and performs an asymptotic analysis of the LPR covariance estimator to show that both the estimation bias and variance are functions of the bandwidth and there exists an optimal bandwidth to minimize the mean square error (MSE) locally. A data-driven variable bandwidth selection method, namely the intersection of confid

SUBMITTER: Fu Z 

PROVIDER: S-EPMC10716865 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature