arXiv research

Neural optimal transport improves multivariate conformal prediction.

This work extends VQR to non-linear cases and provides scalable solvers.

Introduces a new quantile regression method for financial and wage data analysis.

TSVQR captures heterogeneous and asymmetric data using quantile regression.

Novel SVM approach for extreme quantile regression with heavy tailed inputs.

Improved conformalized quantile regression for adaptive prediction intervals.

In this paper, we propose a novel asymmetric $ε$-insensitive pinball loss function for quantile estimation. There exists some pinball loss functions which attempt to incorporate the $ε$-insensitive zone approach in it but, they fail to extend the $ε$-insensitive approach for quantile estimation in true sense. The propo…

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

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

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…

SVR analyzed within RQ framework for risk management.

CQNPs enhance predictive performance and distribution modeling using quantile regression.

Constructs bivariate quantiles using vine copulas for multivariate analysis.

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…

Bayesian method improves quantile estimation and subset selection.

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

New quantile methods improve uncertainty quantification across various models.

Proposes a deep learning method to ensure non-crossing quantiles in conditional distributions.

SLS optimizes minimum-volume regions for conditional quantiles, bypassing density estimation.

Spatio-temporal problems are ubiquitous and of vital importance in many research fields. Despite the potential already demonstrated by deep learning methods in modeling spatio-temporal data, typical approaches tend to focus solely on conditional expectations of the output variables being modeled. In this paper, we prop…

This paper proposes a novel '$ν$-support vector quantile regression' ($ν$-SVQR) model for the quantile estimation. It can facilitate the automatic control over accuracy by creating a suitable asymmetric $ε$-insensitive zone according to the variance present in data. The proposed $ν$-SVQR model uses the $ν$ fraction of …

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…

NQE uses quantile regression for fast SBI with cubic Hermite splines.

TQF models multivariate uncertainty by learning conditional quantiles.

IQ-BART models conditional quantiles using a non-parametric Bayesian approach.

Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in …

Paper introduces arctan pinball loss for XGBoost quantile regression.

SPQR package uses neural networks for flexible quantile regression.

We show how to reduce the process of predicting general order statistics (and the median in particular) to solving classification. The accompanying theoretical statement shows that the regret of the classifier bounds the regret of the quantile regression under a quantile loss. We also test this reduction empirically ag…

Proposes deep quantile regression for uncertainty estimation in lesion detection.

ConquerNet smooths quantile regression for deep learning with minimax guarantees.

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

Optimal inference in distributed quantile regression without stringent scaling conditions.

Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting p…

A new model captures financial asset returns' tail behaviors and outperforms GARCH family.

A scalable PyTorch framework for non-crossing quantile regression.

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

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

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

A new method avoids quantile crossing in time series forecasting.

New method for robustly estimating treatment effects across different risk levels.

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…

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

We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the explorati…

Deep neural networks enforce non-crossing quantile regression curves.

New method solves quantile crossing problem in econometrics.

In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics. We propose a novel parametrization that characterizes any collection of non-crossing quantile planes over arbitrarily shaped convex predictor domains in a…

New method improves conditional coverage of conformal prediction.