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Abstract Details
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Activity Number:
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320
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304322 |
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Title:
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Multivariate Convex Regression
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Author(s):
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Lauren Hannah*+ and David Dunson
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Companies:
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Duke University and Duke University
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Address:
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Box 90251, Durham, NC, 27708,
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Keywords:
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convex regression ;
value function approximation ;
regression ;
shape constrained inference
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Abstract:
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Regression problems with a convexity constraint on the mean function are common in economics, financial engineering, operations research and electrical engineering. In a purely regression setting, convexity constraints can increase predictive accuracy compared to unconstrained regression. In a convex optimization setting, convex regression can be used to approximate objective functions and constraints. However, current convex regression methods are computationally infeasible for moderate to large problems in a multivariate setting. We introduce a computationally efficient new method and give consistency results. We apply the method to value function approximation for decision problems including response surface estimation, pricing American basket options and device modeling for geometric programming based circuit design optimization.
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