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
References in zbMATH (referenced in 142 articles , 1 standard article )
Showing results 1 to 20 of 142.
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- Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
- Brantley, Halley L.; Guinness, Joseph; Chi, Eric C.: Baseline drift estimation for air quality data using quantile trend filtering (2020)
- Daniel Fischer, Karl Mosler, Jyrki Möttönen, Klaus Nordhausen, Oleksii Pokotylo, Daniel Vogel: Computing the Oja Median in R: The Package OjaNP (2020) not zbMATH
- Güney, Yeşim; Jurečková, Jana; Arslan, Olcay: Averaged autoregression quantiles in autoregressive model (2020)
- Jiang, Fei; Cheng, Qing; Yin, Guosheng; Shen, Haipeng: Functional censored quantile regression (2020)
- Jurečková, Jana; Picek, Jan; Schindler, Martin: Empirical regression quantile processes. (2020)
- Liu, Yusha; Li, Meng; Morris, Jeffrey S.: Function-on-scalar quantile regression with application to mass spectrometry proteomics data (2020)
- Navarro, Jorge: Bivariate box plots based on quantile regression curves (2020)
- Plečko, Drago; Meinshausen, Nicolai: Fair data adaptation with quantile preservation (2020)
- Santolino, Miguel: The Lee-Carter quantile mortality model (2020)
- Uribe, Jorge M.; Guillen, Montserrat: Quantile regression for cross-sectional and time series data. Applications in energy markets using R (2020)
- 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)
- Belloni, Alexandre; Chernozhukov, Victor; Chetverikov, Denis; Fernández-Val, Iván: Conditional quantile processes based on series or many regressors (2019)
- Belloni, Alexandre; Chernozhukov, Victor; Kato, Kengo: Valid post-selection inference in high-dimensional approximately sparse quantile regression models (2019)
- Bilias, Yannis; Florios, Kostas; Skouras, Spyros: Exact computation of censored least absolute deviations estimator (2019)
- Bloznelis, Daumantas; Claeskens, Gerda; Zhou, Jing: Composite versus model-averaged quantile regression (2019)
- Escanciano, J. C.; Goh, S. C.: Quantile-regression inference with adaptive control of size (2019)
- Geraci, Marco: Modelling and estimation of nonlinear quantile regression with clustered data (2019)
- Graf, Monique; Marín, J. Miguel; Molina, Isabel: A generalized mixed model for skewed distributions applied to small area estimation (2019)
- Guerra, Maria Letizia; Sorini, Laerte; Stefanini, Luciano: Quantile and expectile smoothing based on (L_1)-norm and (L_2)-norm fuzzy transforms (2019)