NDLI: Simulating Dynamic Covariance Structures for Testing the Adaptive Behavior of Variable Selection Algorithms (Invited Paper)

Content Provider	IEEE Xplore Digital Library
Author	Anagnostopoulos, Christoforos Adams, Niall
Copyright Year	2008
Abstract	Variable selection for regression is a classical statistical problem, motivated by concerns that too large a number of covariates may bring about overfitting and unnecessarily high measurement costs. Novel difficulties arise in streaming contexts, where the correlation structure of the process may be drifting, in which case it must be constantly tracked so that selections may be revised accordingly. A particularly interesting phenomenon is that non-selected covariates become missing variables, inducing bias on subsequent decisions. This raises an intricate exploration-exploitation tradeoff, whose dependence on the covariance tracking algorithm and the choice of variable selection scheme is too complex to be dealt with analytically. We hence capitalise on the strength of simulations to explore this problem, taking the opportunity to tackle the difficult task of simulating dynamic correlation structures.
Starting Page	52
Ending Page	57
File Size	398751
Page Count	6
File Format	PDF
ISBN	0769531148
DOI	10.1109/UKSIM.2008.92
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Costs exploration-exploitation Input variables Computational modeling Computer simulation Stochastic processes Educational institutions Engines covariance simulation missing data variable selection Testing Context modeling
Content Type	Text
Resource Type	Article

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