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- W3186178081 abstract "Penalized spline estimation with discrete difference penalties (P-splines) is a popular estimation method for semiparametric models, but the classical least-squares estimator is highly sensitive to deviations from its ideal model assumptions. To remedy this deficiency, a broad class of P-spline estimators based on general loss functions is introduced and studied. Robust estimators are obtained by well-chosen loss functions, such as the Huber or Tukey loss function. A preliminary scale estimator can also be included in the loss function. It is shown that this class of P-spline estimators enjoys the same optimal asymptotic properties as least-squares P-splines, thereby providing strong theoretical motivation for its use. The proposed estimators may be computed very efficiently through a simple adaptation of well-established iterative least squares algorithms and exhibit excellent performance even in finite samples, as evidenced by a numerical study and a real-data example." @default.
- W3186178081 created "2021-08-02" @default.
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- W3186178081 date "2021-07-01" @default.
- W3186178081 modified "2023-10-18" @default.
- W3186178081 title "Robust penalized spline estimation with difference penalties" @default.
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- W3186178081 doi "https://doi.org/10.1016/j.ecosta.2021.07.005" @default.
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