Regularized simultaneous model selection in multiple quantiles regression
Computational Statistics & Data Analysis
GACV for quantile smoothing splines
Computational Statistics & Data Analysis
Geoadditive expectile regression
Computational Statistics & Data Analysis
On confidence intervals for semiparametric expectile regression
Statistics and Computing
Asymmetric least squares support vector machine classifiers
Computational Statistics & Data Analysis
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Quantiles are computed by optimizing an asymmetrically weighted L"1 norm, i.e. the sum of absolute values of residuals. Expectiles are obtained in a similar way when using an L"2 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.