Learned-loss boosting

Giles Hooker and James O. Ramsay

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 3935-3944

Abstract: This paper considers a problem of jointly estimating a regression function and the distribution of residuals when both are specified non-parametrically. We present a joint penalized optimization criterion that combines log-spline density estimation with spline-based regression methods. We also examine the use of boosting methodology to estimate a regression function over a high dimensional covariate space. We demonstrate that our method has a robustification effect, and show its usefulness in diagnosing problems in data. We illustrate our methods with practical examples when likelihood is an appropriate evaluation criterion.

Keywords: Boosting; Density estimation; Functional data analysis; Robust estimates (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:3935-3944

DOI: 10.1016/j.csda.2012.05.019

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