Expectreg

R package expectreg: Expectile and Quantile Regression. Expectile and quantile regression of models with nonlinear effects e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes as well as boosting; also supplies expectiles for common distributions.

Keywords for this software

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References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Chen, Tuo; Su, Zhihua; Yang, Yi; Ding, Shanshan: Efficient estimation in expectile regression using envelope models (2020)
  2. Eberl, Andreas; Klar, Bernhard: Asymptotic distributions and performance of empirical skewness measures (2020)
  3. Guerra, Maria Letizia; Sorini, Laerte; Stefanini, Luciano: Quantile and expectile smoothing based on (L_1)-norm and (L_2)-norm fuzzy transforms (2019)
  4. Farooq, Muhammad; Steinwart, Ingo: An SVM-like approach for expectile regression (2017)
  5. Schulze Waltrup, Linda; Kauermann, Göran: Smooth expectiles for panel data using penalized splines (2017)
  6. Waldmann, Elisabeth; Sobotka, Fabian; Kneib, Thomas: Bayesian regularisation in geoadditive expectile regression (2017)
  7. Zhang, Feipeng; Li, Qunhua: A continuous threshold expectile model (2017)
  8. Waltrup, Linda Schulze; Sobotka, Fabian; Kneib, Thomas; Kauermann, Göran: Expectile and quantile regression -- David and Goliath? (2015)
  9. Kneib, Thomas: Beyond mean regression (2013)
  10. Rigby, Ra; Stasinopoulos, Dm; Voudouris, V.: Discussion: a comparison of GAMLSS with quantile regression (2013)
  11. Schnabel, Sabine K.; Eilers, Paul H. C.: Simultaneous estimation of quantile curves using quantile sheets (2013)
  12. Sobotka, Fabian; Kauermann, Göran; Waltrup, Linda Schulze; Kneib, Thomas: On confidence intervals for semiparametric expectile regression (2013)
  13. Sobotka, Fabian; Kneib, Thomas: Geoadditive expectile regression (2012)