Optimal expectile smoothing
Computational Statistics & Data Analysis , Volume 53 - Issue 12 p. 4168- 4177
Quantiles are computed by optimizing an asymmetrically weighted L1norm, i.e. the sum of absolute values of residuals. Expectiles are obtained in a similar way when using an L2norm, 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.
|Computational Statistics & Data Analysis|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Schnabel, S.K, & Eilers, P.H.C. (2009). Optimal expectile smoothing. Computational Statistics & Data Analysis, 53(12), 4168–4177. doi:10.1016/j.csda.2009.05.002