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 119 articles , 1 standard article )

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  1. Daniel Fischer, Karl Mosler, Jyrki Möttönen, Klaus Nordhausen, Oleksii Pokotylo, Daniel Vogel: Computing the Oja Median in R: The Package OjaNP (2020) not zbMATH
  2. 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)
  3. Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis; Fernández-Val, Iván: Conditional quantile processes based on series or many regressors (2019)
  4. Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo: Valid post-selection inference in high-dimensional approximately sparse quantile regression models (2019)
  5. Bilias, Yannis; Florios, Kostas; Skouras, Spyros: Exact computation of censored least absolute deviations estimator (2019)
  6. Bloznelis, Daumantas; Claeskens, Gerda; Zhou, Jing: Composite versus model-averaged quantile regression (2019)
  7. Escanciano, J. C.; Goh, S. C.: Quantile-regression inference with adaptive control of size (2019)
  8. Geraci, Marco: Modelling and estimation of nonlinear quantile regression with clustered data (2019)
  9. Graf, Monique; Marín, J. Miguel; Molina, Isabel: A generalized mixed model for skewed distributions applied to small area estimation (2019)
  10. Guerra, Maria Letizia; Sorini, Laerte; Stefanini, Luciano: Quantile and expectile smoothing based on (L_1)-norm and (L_2)-norm fuzzy transforms (2019)
  11. Harding, Matthew; Lamarche, Carlos: A panel quantile approach to attrition bias in big data: evidence from a randomized experiment (2019)
  12. Lin, Yi; Martin, Ryan; Yang, Min: On optimal designs for nonregular models (2019)
  13. Merhi Bleik, Josephine: Fully Bayesian estimation of simultaneous regression quantiles under asymmetric Laplace distribution specification (2019)
  14. Wang, Yafei; Kong, Linglong; Jiang, Bei; Zhou, Xingcai; Yu, Shimei; Zhang, Li; Heo, Giseon: Wavelet-based LASSO in functional linear quantile regression (2019)
  15. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH
  16. Brown, Jonathon D.: Advanced statistics for the behavioral sciences. A computational approach with R (2018)
  17. Chen, Songnian: Sequential estimation of censored quantile regression models (2018)
  18. Das, Priyam; Ghosal, Subhashis: Bayesian non-parametric simultaneous quantile regression for complete and grid data (2018)
  19. Ehm, Werner; Krüger, Fabian: Forecast dominance testing via sign randomization (2018)
  20. El Karoui, Noureddine; Purdom, Elizabeth: Can we trust the bootstrap in high-dimensions? The case of linear models (2018)

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