Events

Andrew Walden, (Imperial College London)

Problems and Solutions in Partial Coherence Estimation
Abstract: Partial coherence is an important quantity in multivariate time series and is used to build time series graphs in areas such as neuroscience. Estimation of this quantity is fraught with difficulties and unless these issues are carefully treated a "garbage-in, garbage-out" scenario is possible. On the pre-processing side we will consider misalignment bias, the removal of spectral lines and filtering. We will then consider statistical problems such as ill-conditioned spectral matrices, spectral leakage, and the statistical distribution of the estimated partial coherence and its consequences.

Mark Fiecas, (University of Warwick)

Two Problems for Multivariate Time Series
Abstract: In this talk, I will discuss two related problems for multivariate time series data. In the first problem, I will consider high-dimensional time series in a regime-switching context. Whenever a state is rarely visited, the estimates of the parameters for that state will be highly unstable. I will discuss a modification of the EM algorithm that will yield more stable estimates of covariance matrices, and consequently, a more reliable filter for reconstructing the hidden state sequence. In the second problem, I will discuss spectral analysis for multivariate time series data. Motivated by the analysis of fMRI data, I will discuss shrinkage estimators for obtaining regularised estimates of the spectral density matrix. The proposed approach includes the development of the multivariate time-frequency toggle bootstrap for time series data, which we use to obtain estimates of the regularisation parameters.

My collaborators for these works are Rainer von Sachs (Universite catholique de Louvain), Jürgen Franke (TU Kaiserslautern), and Joseph Tadjuidje (TU Kaiserslautern).

14.00 - 15.00: Andrew Walden, "Problems and Solutions in Partial Coherence Estimation"
15.00 - 15.30: Coffee break
15.30 - 16.30: Mark Fiecas, "Two Problems for Multivariate Time Series"