quantreg

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 84 articles , 1 standard article )

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  1. Das, Priyam; Ghosal, Subhashis: Bayesian non-parametric simultaneous quantile regression for complete and grid data (2018)
  2. Fan, Yali; Tang, Yanlin; Zhu, Zhongyi: Variable selection in censored quantile regression with high dimensional data (2018)
  3. Gregory, Karl B.; Lahiri, Soumendra N.; Nordman, Daniel J.: A smooth block bootstrap for quantile regression with time series (2018)
  4. Huang, Mei Ling; Nguyen, Christine: A nonparametric approach for quantile regression (2018)
  5. Zheng, Qi; Peng, Limin; He, Xuming: High dimensional censored quantile regression (2018)
  6. Abdelaati Daouia and Thibault Laurent and Hohsuk Noh: npbr: A Package for Nonparametric Boundary Regression in R (2017)
  7. Boček, Pavel; Šiman, Miroslav: On weighted and locally polynomial directional quantile regression (2017)
  8. Chesher, Andrew: Understanding the effect of measurement error on quantile regressions (2017)
  9. Díaz, Iván: Efficient estimation of quantiles in missing data models (2017)
  10. Dries Benoit and Dirk Van den Poel: bayesQR: A Bayesian Approach to Quantile Regression (2017)
  11. Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
  12. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  13. Kuk, Anthony Y. C.: Function compositional adjustments of conditional quantile curves (2017)
  14. Maistre, Samuel; Lavergne, Pascal; Patilea, Valentin: Powerful nonparametric checks for quantile regression (2017)
  15. 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
  16. Xu, Guanglin; Burer, Samuel: A branch-and-bound algorithm for instrumental variable quantile regression (2017)
  17. Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia: Generalized accelerated failure time spatial frailty model for arbitrarily censored data (2017)
  18. Anita Thieler; Roland Fried; Jonathan Rathjens: RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression (2016)
  19. Fuzi, Mohd Fadzli Mohd; Jemain, Abdul Aziz; Ismail, Noriszura: Bayesian quantile regression model for claim count data (2016)
  20. Galvao, Antonio F.; Kato, Kengo: Smoothed quantile regression for panel data (2016)

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