NDLI: Using Convex Optimization for Nonparametric Statistical Analysis of Point Processes

Content Provider	IEEE Xplore Digital Library
Author	Coleman, T.P. Sarma, S.
Copyright Year	2007
Description	Author affiliation: UIUC, Urbana (Coleman, T.P.)
Abstract	Point process models have been shown to be useful in characterizing neural spiking activity as a function of extrinsic and intrinsic factors. Most point process models of neural spiking are parametric as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of functions, misleading inferences can arise. Nonparametric methods are attractive due to fewer assumptions, but most methods require excessively complex algorithms. We propose a computationally efficient method for nonparametric maximum likelihood estimation when the conditional intensity function, which characterizes the point process in its entirety, is assumed to satisfy a Lipschitz continuity condition. We show that by exploiting the structure of the likelihood function of a point process, the problem becomes efficiently solvable via Lagrangian duality and we compare our nonparametric estimation method to the most commonly used parametric approaches on goldfish retinal ganglion neural data. In this example, our nonparametric method gives a superior absolute goodness-of-fit measure than all parametric approaches analyzed.
Starting Page	1476
Ending Page	1480
File Size	932770
Page Count	5
File Format	PDF
ISBN	9781424413973
DOI	10.1109/ISIT.2007.4557135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-24
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis Parametric statistics Retina Neuroscience Laboratories Inference algorithms Maximum likelihood estimation Lagrangian functions Sea measurements History
Content Type	Text
Resource Type	Article

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