arXiv research

PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.

The paper proposes a method to construct well-calibrated prediction sets for correlated target variables.

Bayesian method estimates QTEs from observational data.

The paper introduces a new method for forecasting financial risk using quantile-based modeling.

This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of int…

Additive models play an important role in semiparametric statistics. This paper gives learning rates for regularized kernel based methods for additive models. These learning rates compare favourably in particular in high dimensions to recent results on optimal learning rates for purely nonparametric regularized kernel …

This paper offers a simple method for Bayesian regression with unknown transformations.

This paper improves reinforcement learning by estimating return distributions using quantiles.

This paper improves reinforcement learning by estimating return distributions using quantiles.

We consider concepts and models for measuring inequality in the distribution of resources with a focus on how inequality varies as a function of covariates. Lorenz introduced a device for measuring inequality in the distribution of income that indicates how much the incomes below the u$^{th}$ quantile fall short of the…

Combines VaR and ES forecasts for cryptocurrency market risk management.

Parallel gradient boosting speeds up multi-output regression.

Investigates methods to regularize quantile regression for accurate predictions.

Improved conformalized quantile regression for adaptive prediction intervals.

New algorithm identifies best arm in semiparametric bandits with near optimal efficiency.

New method for semiparametric bandits reduces regret to optimal levels.

The paper proposes a method for predicting equity premium using penalized quantile regression.

We develop quantile regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk ma…

SCQRNN prevents quantile crossing and improves computational efficiency.

Bayesian framework for semiparametric regression of discrete data.

Deep learning method for semiparametric regression of spatial data.

Quantile regression using random forest proximities improves prediction and uncertainty quantification.

Semiparametric STAR model improves mental health data analysis.

fastkqr speeds up kernel quantile regression by up to 10x.

Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a…

Paper introduces arctan pinball loss for XGBoost quantile regression.

Proposes a method to estimate conditional quantiles using both high-fidelity and low-fidelity data.

TSVQR captures heterogeneous and asymmetric data using quantile regression.

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regre…

SPQR package uses neural networks for flexible quantile regression.

The paper decouples shrinkage and selection in Bayesian Quantile Regression.

Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regre…

CQNPs enhance predictive performance and distribution modeling using quantile regression.

Sparse Gaussian process quantile regression tackles computational challenges in Bayesian quantile regression.

Proposes a non-crossing deep neural network quantile regression method.

Hypothesis tests in models whose dimension far exceeds the sample size can be formulated much like the classical studentized tests only after the initial bias of estimation is removed successfully. The theory of debiased estimators can be developed in the context of quantile regression models for a fixed quantile value…

Quantile regression undercovers true uncertainty, revealing a bias in high dimensions.

Quantile regression with ReLU networks achieves minimax rates for various function types.

Bayesian method improves quantile estimation and subset selection.

Paper tackles distributed quantile regression with improved efficiency and support recovery.

RQR improves prediction intervals for skewed data.

Quantile regression is an increasingly important empirical tool in economics and other sciences for analyzing the impact of a set of regressors on the conditional distribution of an outcome. Extremal quantile regression, or quantile regression applied to the tails, is of interest in many economic and financial applicat…

Deep Huber QRNs predict Huber quantiles for house prices.

A scalable PyTorch framework for non-crossing quantile regression.

ConquerNet smooths quantile regression for deep learning with minimax guarantees.

A new method avoids quantile crossing in time series forecasting.

A new method improves quantile regression for high-dimensional data.

Paper proposes differentially private quantile regression for high-dimensional data.