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 148 articles , 1 standard article )
Showing results 1 to 20 of 148.
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- Baione, Fabio; Biancalana, Davide: An application of parametric quantile regression to extend the two-stage quantile regression for ratemaking (2021)
- Hudecová, Šárka; Šiman, Miroslav: Testing axial symmetry by means of directional regression quantiles (2021)
- Maruotti, Antonello; Petrella, Lea; Sposito, Luca: Hidden semi-Markov-switching quantile regression for time series (2021)
- Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
- Petersen, Lasse; Hansen, Niels Richard: Testing conditional independence via quantile regression based partial copulas (2021)
- Pietrosanu, Matthew; Gao, Jueyu; Kong, Linglong; Jiang, Bei; Niu, Di: Advanced algorithms for penalized quantile and composite quantile regression (2021)
- Prajual Maheshwari, Mohammad Arshad Rahman: bqror: An R package for Bayesian Quantile Regression in Ordinal Models (2021) arXiv
- 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)