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Abstract Details
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Activity Number:
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114
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300734 |
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Title:
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Sparse Functional Model and Its Application to Neural Spike Activities
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Author(s):
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Yan Tu and Jay Breidt and Haonan Wang*+
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Companies:
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Colorado State University and Colorado State University and Colorado State University
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Address:
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Department of Statistics, Fort Collins, CO, 80523,
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Keywords:
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Dynamic system ;
Group bridge ;
LASSO ;
Variable selection
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Abstract:
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In this talk, we consider the problem of modeling neural signal transformation. A dynamic Multiple-Input, Single-Output model of neural information communication is proposed. Each input neuron and the output neuron have a functional relationship which is approximated by polynomial splines. A penalized likelihood approach is implemented for simultaneous parameter estimation and variable selection. The notion of sparsity in parameter estimation has been generalized to function estimation. Two different types of functional sparsity are of particular interest: global sparsity and local sparsity. Computation of the penalized approach is rather challenging. The one-step estimator based on the group bridge approach for maximizing the penalized likelihood is proposed. The performance of the proposed method is assessed using Monte Carlo simulation studies.
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