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 126 articles , 1 standard article )

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  1. Brantley, Halley L.; Guinness, Joseph; Chi, Eric C.: Baseline drift estimation for air quality data using quantile trend filtering (2020)
  2. Daniel Fischer, Karl Mosler, Jyrki Möttönen, Klaus Nordhausen, Oleksii Pokotylo, Daniel Vogel: Computing the Oja Median in R: The Package OjaNP (2020) not zbMATH
  3. Jurečková, Jana; Picek, Jan; Schindler, Martin: Empirical regression quantile processes. (2020)
  4. Liu, Yusha; Li, Meng; Morris, Jeffrey S.: Function-on-scalar quantile regression with application to mass spectrometry proteomics data (2020)
  5. Santolino, Miguel: The Lee-Carter quantile mortality model (2020)
  6. Zhang, Likun; del Castillo, Enrique; Berglund, Andrew J.; Tingley, Martin P.; Govind, Nirmal: Computing confidence intervals from massive data via penalized quantile smoothing splines (2020)
  7. Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis; Fernández-Val, Iván: Conditional quantile processes based on series or many regressors (2019)
  8. Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo: Valid post-selection inference in high-dimensional approximately sparse quantile regression models (2019)
  9. Bilias, Yannis; Florios, Kostas; Skouras, Spyros: Exact computation of censored least absolute deviations estimator (2019)
  10. Bloznelis, Daumantas; Claeskens, Gerda; Zhou, Jing: Composite versus model-averaged quantile regression (2019)
  11. Escanciano, J. C.; Goh, S. C.: Quantile-regression inference with adaptive control of size (2019)
  12. Geraci, Marco: Modelling and estimation of nonlinear quantile regression with clustered data (2019)
  13. Graf, Monique; Marín, J. Miguel; Molina, Isabel: A generalized mixed model for skewed distributions applied to small area estimation (2019)
  14. Guerra, Maria Letizia; Sorini, Laerte; Stefanini, Luciano: Quantile and expectile smoothing based on (L_1)-norm and (L_2)-norm fuzzy transforms (2019)
  15. Harding, Matthew; Lamarche, Carlos: A panel quantile approach to attrition bias in big data: evidence from a randomized experiment (2019)
  16. Lin, Yi; Martin, Ryan; Yang, Min: On optimal designs for nonregular models (2019)
  17. Merhi Bleik, Josephine: Fully Bayesian estimation of simultaneous regression quantiles under asymmetric Laplace distribution specification (2019)
  18. Vicendese, D.; Te Marvelde, L.; McNair, P. D.; Whitfield, K.; English, D. R.; Ben Taieb, S.; Hyndman, R. J.; Thomas, R.: Predicting the whole distribution with methods for depth data analysis demonstrated on a colorectal cancer treatment study (2019)
  19. Wang, Yafei; Kong, Linglong; Jiang, Bei; Zhou, Xingcai; Yu, Shimei; Zhang, Li; Heo, Giseon: Wavelet-based LASSO in functional linear quantile regression (2019)
  20. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH

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