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Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

By DJ Albers and George Hripcsak


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

Topics: Nonlinear Sciences - Chaotic Dynamics, Physics - Data Analysis, Statistics and Probability, Quantitative Biology - Quantitative Methods, Statistics - Other Statistics
Publisher: 'Elsevier BV'
Year: 2011
DOI identifier: 10.1016/j.chaos.2012.03.003
OAI identifier:

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