R package quantreg: Quantile Regression. Estimation and inference methods for models of conditional quantiles: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also included. (Source: http://cran.r-project.org/web/packages)

References in zbMATH (referenced in 62 articles )

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  1. Abdelaati Daouia and Thibault Laurent and Hohsuk Noh: npbr: A Package for Nonparametric Boundary Regression in R (2017)
  2. Boček, Pavel; Šiman, Miroslav: On weighted and locally polynomial directional quantile regression (2017)
  3. Chesher, Andrew: Understanding the effect of measurement error on quantile regressions (2017)
  4. Díaz, Iván: Efficient estimation of quantiles in missing data models (2017)
  5. Dries Benoit and Dirk Van den Poel: bayesQR: A Bayesian Approach to Quantile Regression (2017)
  6. Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
  7. Maistre, Samuel; Lavergne, Pascal; Patilea, Valentin: Powerful nonparametric checks for quantile regression (2017)
  8. Matthew Pietrosanu, Jueyu Gao, Linglong Kong, Bei Jiang, Di Niu: cqrReg: An R Package for Quantile and Composite Quantile Regression and Variable Selection (2017) arXiv
  9. Xu, Guanglin; Burer, Samuel: A branch-and-bound algorithm for instrumental variable quantile regression (2017)
  10. Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia: Generalized accelerated failure time spatial frailty model for arbitrarily censored data (2017)
  11. Fuzi, Mohd Fadzli Mohd; Jemain, Abdul Aziz; Ismail, Noriszura: Bayesian quantile regression model for claim count data (2016)
  12. Galvao, Antonio F.; Kato, Kengo: Smoothed quantile regression for panel data (2016)
  13. Mingli Chen, Victor Chernozhukov, Ivan Fernandez-Val, Blaise Melly: Counterfactual: An R Package for Counterfactual Analysis (2016) arXiv
  14. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)
  15. Vincenzo Lagani, Giorgos Athineou, Alessio Farcomeni, Michail Tsagris, Ioannis Tsamardinos: Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsets (2016) arXiv
  16. Harrell, Frank E. jun.: Regression modeling strategies. With applications to linear models, logistic regression, and survival analysis (2015)
  17. Mak, T.K.; Nebebe, F.: A parametric approach for estimating conditional probability distributions (2015)
  18. Qoyyimi, Danang Teguh; Zitikis, Ricardas: Measuring association via lack of co-monotonicity: the loc index and a problem of educational assessment (2015)
  19. Valle, C.A.; Meade, N.; Beasley, J.E.: Factor neutral portfolios (2015)
  20. Kley, Tobias: Quantile-based spectral analysis: asymptotic theory and computation (2014)

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