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 160 articles , 1 standard article )
Showing results 1 to 20 of 160.
Sorted by year (- Andreas Alfons, Nüfer Y. Ateş, Patrick J. F. Groenen: Robust Mediation Analysis: The R Package robmed (2022) arXiv
- Baione, Fabio; Biancalana, Davide: An application of parametric quantile regression to extend the two-stage quantile regression for ratemaking (2021)
- Ditzhaus, Marc; Fried, Roland; Pauly, Markus: QANOVA: quantile-based permutation methods for general factorial designs (2021)
- Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., Goude, Y. : qgam: Bayesian Nonparametric Quantile Regression Modeling in R (2021) not zbMATH
- Frumento, Paolo; Salvati, Nicola: Parametric modeling of quantile regression coefficient functions with count data (2021)
- Galarza, Christian E.; Zhang, Panpan; Lachos, Víctor H.: Logistic quantile regression for bounded outcomes using a family of heavy-tailed distributions (2021)
- Hudecová, Šárka; Šiman, Miroslav: Testing axial symmetry by means of directional regression quantiles (2021)
- Jantre, S. R.; Bhattacharya, S.; Maiti, T.: Quantile regression neural networks: a Bayesian approach (2021)
- Jung, Yoonsuh; MacEachern, Steven N.; Joon Kim, Hang: Modified check loss for efficient estimation via model selection in quantile regression (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
- Muggeo, Vito M. R.; Torretta, Federico; Eilers, Paul H. C.; Sciandra, Mariangela; Attanasio, Massimo: Multiple smoothing parameters selection in additive regression quantiles (2021)
- Perperoglou, Aris; Huebner, Marianne: Quantile foliation for modelling performance across body mass and age in olympic weightlifting (2021)
- 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
- Santolino, Miguel: Median bilinear models in presence of extreme values (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)