Point-process principal components analysis via geometric optimization

Authors:
Victor Solo;Syed Ahmed Pasha
Affiliations:
-;-
Venue:
Neural Computation
Year:
2013

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Abstract

There has been a fast-growing demand for analysis tools for multivariate point-process data driven by work in neural coding and, more recently, high-frequency finance. Here we develop a true or exact as opposed to one based on time binning principal components analysis for preliminary processing of multivariate point processes. We provide a maximum likelihood estimator, an algorithm for maximization involving steepest ascent on two Stiefel manifolds, and novel constrained asymptotic analysis. The method is illustrated with a simulation and compared with a binning approach.