Optimal expectile smoothing

Sabine K. Schnabel and Paul H.C. Eilers

Computational Statistics & Data Analysis, 2009, vol. 53, issue 12, 4168-4177

Abstract: Quantiles are computed by optimizing an asymmetrically weighted L1 norm, i.e. the sum of absolute values of residuals. Expectiles are obtained in a similar way when using an L2 norm, i.e. the sum of squares. Computation is extremely simple: weighted regression leads to the global minimum in a handful of iterations. Least asymmetrically weighted squares are combined with P-splines to compute smooth expectile curves. Asymmetric cross-validation and the Schall algorithm for mixed models allow efficient optimization of the smoothing parameter. Performance is illustrated on simulated and empirical data.

Date: 2009
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