Quantile Regression

By J. Jurečková. 2006

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In Statistics and Probability

Citations in the corpus

Offline Reinforcement Learning with Implicit Q-Learning (2021, methodology): “Expectile regression is closely related to quantile regression (Koenker & Hallock, 2001), which is a popular technique for estimating quantiles of a distribution widely used in reinforcement learning (Dabney et al., 2018b;a) .”
Mathematical optimization in classification and regression trees (2021, neutral): “Hallock 2001; Kriegler and Berk 2010).”
Distributional Reinforcement Learning with Quantile Regression (2017, methodology): “Quantile regression , and conditional quantile regression , are methods for approximating the quantile functions of distributions and conditional distributions respectively (Koenker 2005).”
Implicit Quantile Networks for Distributional Reinforcement Learning (2018, methodology): “This estimation uses quantile regression , which has been shown to converge to the true quantile function value when minimized using stochastic approximation (Koenker, 2005).”
Feature-based tuning of simulated annealing applied to the curriculum-based course timetabling problem (2014, methodology): “The model among M 1 , M 2 , M 3 that provided the best ﬁt was selected by means of the Akaike Information Criterion (AIC) for model selection (Koenker, 2005, Section 4.9.1), which is known to have good performances for prediction.”
Dualism and cross-country growth regressions (2006, background): “16See Koenker and Hallock (2001) for an introduction to quantile regression.”
Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions (2013, methodology): “Section 4 considers the estimation of conditional quantile functions which constitute an extremely popular estimation methodology (Koenker (2005)) and may be predicated directly on an estimated conditional CDF as proposed by Li & Racine (2008). Section 5 assesses the finite sample performance of the”
POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES (2011, methodology): “For example, Rousseau (2010) and Kruijer et al. (2010) show that in univariate density estimation, Bayesian models based on mixtures of distributions automatically adapt to the smoothness of the target density and deliver minimax convergence rates up to a logarithmic factor. They also demonstrate th”
Conformalized Quantile Regression (2019, background): “As in classical regression, one can leverage the great variety of machine learning methods to design and learn ˆ q α [19–21,23,30].”
Spillovers from Multinationals to Heterogeneous Domestic Firms: Evidence from Hungary (2009, unknown): “The bootstrapped variance-covariance matrix takes into account the errors correlation between the different quantiles and allows us to compare coefficients of the explanatory variables in the different quantiles (Koenker & Hallock 2001).”
Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, background): “More precisely, the individual point forecasts and the corresponding observation (here: electric load, yt) are put in a standard quantile regression setting, being treated as independent variables and the dependent variable respectively [27].”
Unconditional Quantile Treatment Effects Under Endogeneity (2007, unknown): “This robustness was the first motivation for considering median instead of mean regression in Koenker and Bassett (1978), see also the discussion in Koenker (2005).”