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• W1975041502 abstract "Kernel density estimates are a robust way to reconstruct a continuous distribution from a discrete point set. Typically their effectiveness is measured either in L1 or L2 error. In this paper we investigate the challenges in using L∞ (or worst case) error, a stronger measure than L1 or L2. We present efficient solutions to two linked challenges: how to evaluate the L∞ error between two kernel density estimates and how to choose the bandwidth parameter for a kernel density estimate built on a subsample of a large data set." @default.
• W1975041502 created "2016-06-24" @default.
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• W1975041502 creator A5024285838 @default.
• W1975041502 date "2015-08-10" @default.
• W1975041502 modified "2023-09-24" @default.
• W1975041502 title "L∞ Error and Bandwidth Selection for Kernel Density Estimates of Large Data" @default.
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• W1975041502 doi "https://doi.org/10.1145/2783258.2783357" @default.
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