HS-BQR extends horseshoe prior for Bayesian quantile regression.
problem Estimating quantiles in high-dimensional data with bias and error.
method Horseshoe prior for Bayesian quantile regression with a fast sampling algorithm.
result HS-BQR outperforms other shrinkage priors in coefficient bias and forecast error.
This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …
Combination of distributional regression algorithms improves uncertainty estimation of satellite precipitation products.
problem Uncertainty estimation in satellite precipitation products.
method Ensemble learning methods combining conditional zero-adjusted probability distributions estimated with GAMLSS, spline-based GAMLSS, and distributional regression forests.
result Stacking of methods outperformed individual methods in most quantile levels using the quantile loss function.
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
problem Forecasting oil prices across different quantiles for better risk assessment.
method Quantile Bayesian Vector Autoregression (QBVAR) model.
result QBVAR improves median forecasts by 2-5% and left-tail forecast improvements of 10-25% during crisis episodes.
Estimates causal effects using machine learning for binary treatment and mediator.
problem Estimating direct and indirect quantile treatment effects under selection-on-observables.
method Double/debiased machine learning estimators based on efficient score functions.
result Uniform consistency and asymptotic normality of effect estimators.
Paper evaluates dynamic QTE for ridesharing data.
problem Assessing QTE in ridesharing with skewed outcomes.
method Developed VCDP models to estimate dynamic CQTE.
result Dynamic CQTE equals sum of individual CQTEs.
Investigates methods to regularize quantile regression for accurate predictions.
problem Improving accuracy and fairness in quantile regression predictions.
method Various regularization techniques including expected pinball loss, monotonicity constraints, and rate constraints.
result Deep lattice networks can maintain non-crossing quantiles and improve calibration and fairness.
RQR improves prediction intervals for skewed data.
problem Invalid prediction intervals for skewed noise.
method Relaxed Quantile Regression (RQR) for asymmetric noise.
result Improved prediction intervals with desirable qualities.
Private estimation of many quantiles using differential privacy.
problem Estimating quantiles of a distribution privately.
method Two approaches: 1) Private estimation of empirical quantiles, 2) Uniform density estimation.
result There is a tradeoff between estimating quantiles at specific points and uniformly estimating the quantile function.
The paper develops robust risk measures for uncertain loss positions.
problem Risk assessment for loss positions with uncertain distributions.
method Robust optimized certainty equivalents and generalized quantiles are proposed and analyzed.
result Robust expectiles with specific penalization functions are coherent risk measures.
SPQR package uses neural networks for flexible quantile regression.
problem Flexible modeling of non-linear relationships in quantile regression.
method Monotonic splines and neural networks for density estimation; model-agnostic covariate effects.
result Allows for non-linear and quantile-specific effects.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
problem Forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) for financial returns.
method Semiparametric approach using restricted quantile regression to model the conditional scale of financial returns.
result The method provides robust, distribution-free estimates of extreme losses and captures risk dynamics.
Study federates measurement of demographic disparities from quantile sketches.
problem Misalignment of fairness goals with siloed data collection and privacy regulations.
method Federated auditing of demographic parity through score distributions, using Wasserstein--Frechet variance and quantile summaries.
result Proposes a one-shot, communication-efficient protocol to estimate global disparity and its decomposition.
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…
New method combines CATE and CQTE to estimate treatment effects across different quantiles.
problem Challenges in estimating CQTE due to its dependence on smoothness of individual quantiles.
method Introduces a new estimand, the conditional quantile comparator (CQC), which retains information about the whole treatment distribution and leverages simplicity.
result Demonstrates improved accuracy in estimating treatment effects across different quantiles compared to existing methods.
Bayesian method improves quantile estimation and subset selection.
problem Estimating specific percentiles of the response distribution.
method Bayesian decision analysis perspective, optimal point estimates, interpretable uncertainty quantification, scalable subset selection.
result Substantial gains in quantile estimation accuracy, inference, and variable selection over competitors.
Introduces NQ network for non-crossing quantile learning.
problem Quantile crossing issue in distributional learning.
method Non-negative activation functions ensure monotonic distributions.
result Effective for distributional reinforcement learning and causal effect estimation.
The paper decouples shrinkage and selection in Bayesian Quantile Regression.
problem Improving prediction accuracy in high-dimensional Bayesian Quantile Regression.
method Two-step procedure: shrinkage through continuous priors, sparsification through SAVS.
result The method reduces bias and provides interpretable variable selection.
Paper proposes inference method for high-dimensional censored quantile regression.
problem Identifying heterogeneous effects of high-dimensional genetic biomarkers on survival outcomes.
method Combines low-dimensional model estimates based on multi-sample splittings and variable selection.
result Proposed estimator is consistent and asymptotically follows a Gaussian process.
Paper proposes real-time VaR estimation using quantile regression forest with conformal calibration.
problem Real-time estimation of Value at Risk (VaR) in rapidly changing markets.
method Quantile regression forest trained offline, real-time VaR estimates via observed risk factors, conformalized estimator for reliability.
result The proposed method provides reliable real-time VaR estimates.
Study efficient inference for network quantile causal effects with partial interference.
problem Estimating network causal effects on outcome quantiles with partial interference.
method Developed a nonparametric efficiency theory and a nonparametrically efficient estimator using a three-way cross-fitting procedure.
result Proposed estimator is consistent, asymptotically normal, and allows flexible estimation of nuisance functions.
Robust clustering methods for multivariate time series data.
problem Clustering multivariate time series data robustly to outliers.
method Quantile-based fuzzy C-means with metric, noise, and trimmed approaches.
result Robust methods outperform alternatives in handling outlying series.
This paper develops a new method to model treatment effects that are heterogeneous across different quantiles.
problem Modeling treatment effects that vary across different quantiles of the outcome distribution.
method The paper combines quantile classification with local polynomial estimation to build a decision tree and forest.
result The proposed QLPRT and QLPRF methods provide a new way to estimate and infer heterogeneous treatment effects.
A new, low-cost method speeds up membership inference attacks on large language models.
problem Membership inference attacks on large language models.
method An ensemble of small quantile regression models to determine model training set membership.
result Comparable or improved accuracy with significantly reduced computational cost.
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…
A new algorithm speeds up sparse-penalized quantile regression solving non-convex penalties.
problem Sparse-penalized quantile regression with non-convex penalties.
method Single-loop smoothing ADMM (SIAD) algorithm for faster convergence.
result SIAD method outperforms existing approaches in solving sparse-penalized quantile regression.
TSVQR captures heterogeneous and asymmetric data using quantile regression.
problem Capturing heterogeneous and asymmetric information in modern data.
method Twin Support Vector Quantile Regression (TSVQR) with two nonparallel planes for quantile levels.
result TSVQR outperforms previous methods in capturing and learning from data.
LALR adapts learning rate for faster convergence in regression and neural nets.
problem Finding optimal learning rates for faster convergence in regression and neural networks.
method Lipschitz continuity theory applied to Mean Absolute Error and Quantile loss functions.
result Adaptive learning rate policy enables up to 20x faster convergence.
Develops HCQRF for estimating heterogeneous treatment effects with censored data.
problem Estimating heterogeneous treatment effects on censored responses with high-dimensional variables.
method Hybrid Censored Quantile Regression Forest (HCQRF) combining random forests and censored quantile regression.
result Demonstrates the effectiveness and stability of HCQRF through simulation studies and real-world application.
This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.
problem Analyzing quantiles of heavy-tailed distributions with estimated parameters.
method Introduces a Q-Q orthogonality formulation to separate projection-direction and quantile-threshold effects.
result Decomposes the difference between empirical and population quantiles into three terms.
A new method avoids quantile crossing in time series forecasting.
problem Quantile crossing in joint quantile regressions.
method Incremental (Spline) Quantile Functions (I(S)QF) with neural network.
result Improves consistency and accuracy in time series forecasting.
Paper proposes a method to estimate multiple dynamic quantiles jointly.
problem Limited joint estimation of multiple dynamic quantiles.
method Introduces a crossing penalty objective function for joint estimation.
result Validation through Monte Carlo experiments and empirical application on FTSE100 shows effectiveness.
Bayesian method for estimating quantile sets efficiently.
problem Estimating quantile sets of expensive-to-evaluate functions.
method Bayesian active learning with Gaussian process modeling and Expected Estimator Modification (EEM).
result Efficient estimation of small quantile sets.
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…
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…
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…
Paper introduces arctan pinball loss for XGBoost quantile regression.
problem Efficiently predicting multiple quantiles with XGBoost.
method Smooth approximation of pinball loss for XGBoost, using arctan pinball loss.
result Arctan pinball loss reduces quantile crossings and improves efficiency.
New method for robustly estimating treatment effects across different risk levels.
problem Missing risks and tail events in CATE, especially in aggregate analyses.
method Constructing a pseudo-outcome and regressing it on covariates using any regression learner.
result Robust and model-agnostic learning of conditional distributional treatment effects (CDTE).
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…
The paper proposes a method for predicting equity premium using penalized quantile regression.
problem Heteroscedasticity and heavy-tails in equity premium prediction.
method Penalized quantile regression with consistent variable selection across multiple quantiles.
result The proposed method outperforms benchmark methods and reveals interesting predictor relationships.
A novel fuzzy clustering method for multivariate time series.
problem Clustering multivariate time series with varying dependencies and dynamics.
method Quantile-based cross-spectral features, PCA, fuzzy C-means, fuzzy C-medoids.
result Substantially outperforms existing methods in various evaluation schemes.
NQE uses quantile regression for fast SBI with cubic Hermite splines.
problem Efficient Bayesian inference for complex models with limited data.
method Neural Quantile Estimation (NQE) learns quantiles autoregressively and interpolates them using cubic Hermite splines.
result NQE achieves state-of-the-art performance on various benchmark problems.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
Study quantile reward identification with 1-bit feedback constraints.
problem Best arm identification with quantile reward and 1-bit communication.
method Proposes an algorithm using noisy binary search for quantile reward estimation.
result Derives upper and lower bounds on sample complexity for 1-bit feedback.
QPE identifies causal effects without assuming mechanisms or noise.
problem Identifying causal relationships from observational data.
method Quantile Partial Effect (QPE) and Fisher Information.
result Causal directions can be distinguished using QPE and Fisher Information.
In the paper a problem of risk measures on a discrete-time market model with transaction costs is studied. Strategy effectiveness and shortfall risk is introduced. This paper is a generalization of quantile hedging presented in [4].
Value-at-Risk (VaR) is an institutional measure of risk favored by financial regulators. VaR may be interpreted as a quantile of future portfolio values conditional on the information available, where the most common quantile used is 95%. Here we demonstrate Conditional Autoregressive Value at Risk, first introduced by…
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…