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)

Keywords for this software

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References in zbMATH (referenced in 99 articles , 1 standard article )

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  1. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH
  2. Brown, Jonathon D.: Advanced statistics for the behavioral sciences. A computational approach with R (2018)
  3. Chen, Songnian: Sequential estimation of censored quantile regression models (2018)
  4. Das, Priyam; Ghosal, Subhashis: Bayesian non-parametric simultaneous quantile regression for complete and grid data (2018)
  5. Ehm, Werner; Krüger, Fabian: Forecast dominance testing via sign randomization (2018)
  6. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)
  7. Fan, Yali; Tang, Yanlin; Zhu, Zhongyi: Variable selection in censored quantile regression with high dimensional data (2018)
  8. Gregory, Karl B.; Lahiri, Soumendra N.; Nordman, Daniel J.: A smooth block bootstrap for quantile regression with time series (2018)
  9. Heras, Antonio; Moreno, Ignacio; Vilar-Zanón, José L.: An application of two-stage quantile regression to insurance ratemaking (2018)
  10. Huang, Mei Ling; Nguyen, Christine: A nonparametric approach for quantile regression (2018)
  11. Lee, Sokbae; Liao, Yuan; Seo, Myung Hwan; Shin, Youngki: Oracle estimation of a change point in high-dimensional quantile regression (2018)
  12. Zheng, Qi; Peng, Limin; He, Xuming: High dimensional censored quantile regression (2018)
  13. Abdelaati Daouia and Thibault Laurent and Hohsuk Noh: npbr: A Package for Nonparametric Boundary Regression in R (2017) not zbMATH
  14. Boček, Pavel; Šiman, Miroslav: On weighted and locally polynomial directional quantile regression (2017)
  15. Chesher, Andrew: Understanding the effect of measurement error on quantile regressions (2017)
  16. Díaz, Iván: Efficient estimation of quantiles in missing data models (2017)
  17. Dries Benoit and Dirk Van den Poel: bayesQR: A Bayesian Approach to Quantile Regression (2017) not zbMATH
  18. Goldman, Matt; Kaplan, David M.: Fractional order statistic approximation for nonparametric conditional quantile inference (2017)
  19. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  20. Kuk, Anthony Y. C.: Function compositional adjustments of conditional quantile curves (2017)

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