arXiv research

Bayesian method for estimating inputs leading to specific probability outputs.

Unified approach for estimating quantiles of potential outcomes using inverse estimating equations.

It has long been agreed by academics that the inversion method is the method of choice for generating random variates, given the availability of the quantile function. However for several probability distributions arising in practice a satisfactory method of approximating these functions is not available. The main focu…

We consider the pricing and hedging of exotic options in a model-independent set-up using \emph{shortfall risk and quantiles}. We assume that the marginal distributions at certain times are given. This is tantamount to calibrating the model to call options with discrete set of maturities but a continuum of strikes. In …

Framework uses IRL and RL to elicit and optimize risk preferences robustly to noise.

New formula for implied volatility from Black-Scholes model.

Investigates methods to regularize quantile regression for accurate predictions.

Private estimation of many quantiles using differential privacy.

Optimal inference in distributed quantile regression without stringent scaling conditions.

Deep learning improves quantile regression for censored survival data.

Improved method for private quantile estimation in datasets with atoms.

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

Paper finds robust $Λ$-quantiles equal to extremal distributions.

Sequential quantile estimation refers to incorporating observations into quantile estimates in an incremental fashion thus furnishing an online estimate of one or more quantiles at any given point in time. Sequential quantile estimation is also known as online quantile estimation. This area is relevant to the analysis …

TSVQR captures heterogeneous and asymmetric data using quantile regression.

Develops a new method for sampling from Bayesian credible sets using deep generative quantile learning.

We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step p…

This paper improves credit line impact analysis by considering spending as a distribution.

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…

A new method avoids quantile crossing in time series forecasting.

A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.

Improved conformalized quantile regression for adaptive prediction intervals.

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

One-shot federated learning method for prediction sets.

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

Directly estimates CQC, improving interpretability and accuracy.

Supervised learning is an active research area, with numerous applications in diverse fields such as data analytics, computer vision, speech and audio processing, and image understanding. In most cases, the loss functions used in machine learning assume symmetric noise models, and seek to estimate the unknown function …

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

New quantile methods improve uncertainty quantification across various models.

We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation. Specifically, the output of quantile regression networks is expanded from a set of fixed quantiles to the whole Quantile Function by a univar…

Bayesian method for estimating quantile sets efficiently.

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…

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…

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…

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

New framework calibrates computer models using deep learning and quantile regression.

Study minimax linear regression under quantile risk, improving existing bounds and providing new results.

Constructs bivariate quantiles using vine copulas for multivariate analysis.

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

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

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

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

Proposes deep quantile regression for uncertainty estimation in lesion detection.

Improved quantile estimation using semi-supervised data.

New framework forecasts ES using weighted quantiles.

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…

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

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…