  A method to estimate the time-dependent correlation via an empirical bias
estimate of the time-delayed mutual information for a time-series is proposed.
In particular, the bias of the time-delayed mutual information is shown to
often be equivalent to the mutual information between two distributions of
points from the same system separated by infinite time. Thus intuitively,
estimation of the bias is reduced to estimation of the mutual information
between distributions of data points separated by large time intervals. The
proposed bias estimation techniques are shown to work for Lorenz equations data
and glucose time series data of three patients from the Columbia University
Medical Center database.
