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• W2103183297 abstract "We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there is currently no multivariate method that is stable and computationally feasible for more than a few thousand observations. We introduce convex adaptive partitioning (CAP), which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions. CAP is a computationally efficient, consistent method for convex regression. We demonstrate empirical performance by comparing the performance of CAP to other shape-constrained and unconstrained regression methods for predicting weekly wages and value function approximation for pricing American basket options." @default.
• W2103183297 created "2016-06-24" @default.
• W2103183297 creator A5002768909 @default.
• W2103183297 creator A5004208926 @default.
• W2103183297 date "2013-01-01" @default.
• W2103183297 modified "2023-09-30" @default.
• W2103183297 title "Multivariate convex regression with adaptive partitioning" @default.
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• W2103183297 doi "https://doi.org/10.5555/2567709.2567767" @default.
• W2103183297 hasPublicationYear "2013" @default.
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