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 144 articles , 1 standard article )

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  1. Maruotti, Antonello; Petrella, Lea; Sposito, Luca: Hidden semi-Markov-switching quantile regression for time series (2021)
  2. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  3. Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
  4. Brantley, Halley L.; Guinness, Joseph; Chi, Eric C.: Baseline drift estimation for air quality data using quantile trend filtering (2020)
  5. Daniel Fischer, Karl Mosler, Jyrki Möttönen, Klaus Nordhausen, Oleksii Pokotylo, Daniel Vogel: Computing the Oja Median in R: The Package OjaNP (2020) not zbMATH
  6. Güney, Yeşim; Jurečková, Jana; Arslan, Olcay: Averaged autoregression quantiles in autoregressive model (2020)
  7. Jiang, Fei; Cheng, Qing; Yin, Guosheng; Shen, Haipeng: Functional censored quantile regression (2020)
  8. Jurečková, Jana; Picek, Jan; Schindler, Martin: Empirical regression quantile processes. (2020)
  9. Liu, Yusha; Li, Meng; Morris, Jeffrey S.: Function-on-scalar quantile regression with application to mass spectrometry proteomics data (2020)
  10. Navarro, Jorge: Bivariate box plots based on quantile regression curves (2020)
  11. Plečko, Drago; Meinshausen, Nicolai: Fair data adaptation with quantile preservation (2020)
  12. Santolino, Miguel: The Lee-Carter quantile mortality model (2020)
  13. Uribe, Jorge M.; Guillen, Montserrat: Quantile regression for cross-sectional and time series data. Applications in energy markets using R (2020)
  14. 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)
  15. Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis; Fernández-Val, Iván: Conditional quantile processes based on series or many regressors (2019)
  16. Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo: Valid post-selection inference in high-dimensional approximately sparse quantile regression models (2019)
  17. Bilias, Yannis; Florios, Kostas; Skouras, Spyros: Exact computation of censored least absolute deviations estimator (2019)
  18. Bloznelis, Daumantas; Claeskens, Gerda; Zhou, Jing: Composite versus model-averaged quantile regression (2019)
  19. Escanciano, J. C.; Goh, S. C.: Quantile-regression inference with adaptive control of size (2019)
  20. Geraci, Marco: Modelling and estimation of nonlinear quantile regression with clustered data (2019)

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