arXiv research

This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.

We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to…

Bayesian method learns from aggregated quantile data.

The study improves VaR forecast accuracy by modeling conditional quantile dynamics.

Develops new algorithms for QRF to handle mixed-frequency and longitudinal 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…

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…

Researchers define quantiles on Riemannian manifolds using optimal transport.

Improved quantile estimation model for VaR.

Paper proposes a method to estimate multiple dynamic quantiles jointly.

PCA-Guided Quantile Sampling preserves data structure in large datasets.

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

Private estimation of many quantiles using differential privacy.

We propose confidence sequences -- sequences of confidence intervals which are valid uniformly over time -- for quantiles of any distribution over a complete, fully-ordered set, based on a stream of i.i.d. observations. We give methods both for tracking a fixed quantile and for tracking all quantiles simultaneously. Sp…

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

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

A new method for optimizing hyperparameters using conformalized quantile regression.

Proposes a method to achieve quantile fairness in predictions.

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

Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression. Based on this theory, we develop Bi…

Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations de…

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…

Proposes QQE for transforming and embedding data distributions.

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…

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…

Paper introduces DQPOPE for estimating return distributions in reinforcement learning.

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…

QFIL improves offline RL by filtering data to reduce bias and variance.

The major perspective of this paper is to provide more evidence into the empirical determinants of capital structure adjustment in different macroeconomics states by focusing and discussing the relative importance of firm-specific and macroeconomic characteristics from an alternative scope in U.S. This study extends th…

Paper introduces a new robust loss function for RL.

We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension $p$ can grow exponentially fast with the sample size $n$. Our method combines the de-biasing technique with the composite quantile function to construct an estimator that …

The paper studies quantile contributions and their relationship with order statistics in heavy-tailed distributions.

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 …

New method for risk quantification using quantile processes and measure distortions.

RQR improves prediction intervals for skewed data.

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

This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment.…

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

Paper proposes a method for predicting any quantile of short-term electricity demand.

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 …

Pairwise quantile regression tackles similarity scoring in biometric systems.

Improves quantile regression models by aggregating multiple models.

This paper analyzes convergence of DP-SGD with adaptive quantile clipping.

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

A new DP approach for Conformal Prediction using quantile search.

Quantile Temporal-Difference learning proved convergent with proof.

Quantile normalisation is a popular normalisation method for data subject to unwanted variations such as images, speech, or genomic data. It applies a monotonic transformation to the feature values of each sample to ensure that after normalisation, they follow the same target distribution for each sample. Choosing a "g…

We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, …