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.
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.
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.
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.
Proposes a transfer learning method for high-dimensional quantile regression.
problem Inadequate handling of heterogeneity and heavy tails in transfer learning.
method High-dimensional quantile regression framework with double transfer learning estimator.
result Established error bounds and valid confidence intervals for high-dimensional quantile regression coefficients.
Framework improves target domain prediction using quantile matching.
problem Improving prediction accuracy in data-scarce target domains.
method Conditional quantile matching for distributional alignment.
result Empirical risk minimizer achieves tighter excess risk bound.
AIHT improves online high-dimensional quantile regression by separating support discovery and refinement.
problem Online high-dimensional quantile regression with structural sparsity.
method Adaptive Iterative Hard Thresholding (AIHT) alternates stochastic updates with adaptive hard-thresholding steps.
result AIHT achieves logarithmic regret for the sliding-window objective in high-dimensional settings.
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.
CQNPs enhance predictive performance and distribution modeling using quantile regression.
problem Limited predictive likelihood of Gaussian models for complex distributions.
method Introducing Conditional Quantile Neural Processes (CQNPs) that focus on estimating informative quantiles.
result Significant improvements in predictive performance and better modeling of multimodal distributions.
Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.
problem Need for uncertainty quantification with multiple confidence levels in diverse applications.
method Online optimization perspective to enforce nestedness of prediction sets while controlling quantile estimation error.
result Achieves stable coverage across all levels, strictly nested prediction sets, and improved efficiency.
fastkqr speeds up kernel quantile regression by up to 10x.
problem Huge computational demands of kernel quantile regression.
method A novel finite smoothing algorithm and spectral technique.
result Significantly faster computation of quantile regression.
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…
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.
Study shows pooling scores for conformal prediction distorts group coverage.
problem Pooling scores for conformal prediction distorts group coverage.
method Derived conservation law and lower bound, demonstrated tension between fairness definitions, quantified trade-off between policies.
result Pooling scores for conformal prediction distorts group coverage.
This paper develops statistical models for cryptocurrency returns using hidden Markov regression and copulas.
problem Capturing the interrelationships and serial heterogeneity of cryptocurrency returns.
method Hidden Markov regression models with regime-switching copulas for quantiles and expectiles.
result Captures extreme returns and their temporal evolution through a latent Markov chain.
Proposes a non-crossing deep neural network quantile regression method.
problem Quantile crossing in nonparametric quantile regression.
method Non-crossing constraints via rectified linear unit penalty function.
result Established non-asymptotic upper bounds for excess risk.
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogen…
Paper tackles distributed quantile regression with improved efficiency and support recovery.
problem Challenges in distributed estimation and support recovery for high-dimensional linear quantile regression.
method Transformed quantile regression into least-squares optimization, applied double-smoothing approach, developed efficient algorithm.
result Achieved near-oracle convergence rate and high support recovery accuracy.
Directly estimates CQC, improving interpretability and accuracy.
problem Inability to model and interpret CQC due to inversion issue.
method Direct doubly robust estimation of CQC without inversion.
result Improved estimation accuracy and interpretability.
Paper proposes a federated learning method for quantile inference with local differential privacy.
problem Federated learning of quantile inference under local differential privacy constraints.
method Local stochastic gradient descent with randomized mechanism for privacy and efficiency.
result Asymptotic normality and functional central limit theorem for the proposed estimator.
Paper proposes differentially private quantile regression for high-dimensional data.
problem Privacy concerns in big data with heterogeneous sensitive personal information.
method Newton-type transformation for reformulating quantile regression into an OLS problem; iterative updates for estimation; debiased estimator for inference; communication-efficient bootstrap.
result Near-optimal statistical accuracy and formal privacy guarantees achieved.
PSQRNN model forecasts electricity consumption in China by integrating neural networks and quantile regression.
problem Electricity forecasting in China due to regional economic, social, and natural conditions.
method PSQRNN combines neural networks and semiparametric quantile regression to model electricity consumption.
result PSQRNN model outperforms traditional methods in forecasting electricity consumption in China.
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.
LCMQR improves prediction intervals by adapting to local heteroscedasticity.
problem Efficient and adaptive prediction intervals for local heteroscedasticity.
method LCMQR combines multi-quantile information with kernel-based localization.
result LCMQR constructs tighter intervals than prior methods, especially in heterogeneous environments.
Study examines grain futures connectedness during Russia-Ukraine conflict.
problem Quantile return connectedness of grain futures markets during geopolitical instability.
method Dynamic quantile VAR combined with frequency-domain decomposition.
result Heterogeneous spillovers across quantiles, with strong transmitters and persistent receivers.
The risk premium of a policy is the sum of the pure premium and the risk loading. In the classification ratemaking process, generalized linear models are usually used to calculate pure premiums, and various premium principles are applied to derive the risk loadings. No matter which premium principle is used, some risk …
This paper improves kernel quantile regression with random features for handling heavy-tailed noises.
problem Handling heavy-tailed noises in kernel quantile regression.
method Introduces a refined error decomposition and establishes a novel connection between KQR-RF and KRR-RF.
result Establishes capacity-dependent learning rates for KQR-RF under mild conditions on the number of random features, which are minimax optimal up to some logarithmic factors.
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention's effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides no mechanism to identify which subpopulations are …
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.…
A new approach to VAEs tackles variance shrinkage using quantile regression.
problem Variance shrinkage in VAEs leads to underestimation of uncertainty.
method Using quantile regression to estimate mean and variance, avoiding shrinkage.
result Our approach effectively detects anomalies and improves lesion detection.
The paper proposes a new policy for optimal treatment allocation based on quantile treatment effects.
problem Optimal treatment allocation policies that target distributional welfare, especially when individuals are heterogeneous.
method The approach involves allocating treatments based on the conditional quantile of individual treatment effects (QoTE), considering both prudent and negligent policymakers.
result The proposed minimax policies are robust to model uncertainty and can be generalized to various settings.
Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.
problem Challenges in uncertainty quantification for multi-agent settings.
method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.
result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.
In regression tasks, aleatoric uncertainty is commonly addressed by considering a parametric distribution of the output variable, which is based on strong assumptions such as symmetry, unimodality or by supposing a restricted shape. These assumptions are too limited in scenarios where complex shapes, strong skews or mu…
Paper proposes a robust method for federated ICA with geometric median aggregation.
problem Federated ICA with permutation ambiguity in client estimations.
method Geometric median aggregation with k-means clustering to resolve permutation ambiguity.
result The method provably remains effective in highly heterogeneous scenarios.
This paper investigates how two important sources of risk -- market tail risk and extreme market volatility risk -- are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral beta representation of risk based on the decomposition of cova…
Proposes LSCP for spatial data uncertainty quantification.
problem Uncertainty quantification in spatial statistics, especially for complex and heterogeneous datasets.
method Localized quantile regression for spatial conformal prediction.
result LSCP provides more accurate and consistent prediction intervals.
The paper challenges the use of decision trees for pointwise inference due to slow convergence rates.
problem The slow convergence rates of decision trees in uniform norm, especially with non-vanishing probability.
method Demonstrates the limitations of adaptive recursive partitioning and shows how random forests can improve performance.
result Decision trees can fail to achieve polynomial rates of convergence in uniform norm, even with pruning.
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.
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.
New risk measures for quantiles under ambiguity improve risk sharing.
problem Risk optimization under ambiguity using quantiles.
method Introducing Choquet quantiles and Choquet Expected Shortfall.
result Optimal allocations for quantile agents under ambiguity.
Paper finds robust Λ-quantiles equal to extremal distributions.
problem Investigating robust models for Λ-quantiles with partial loss information. method Extending classical quantiles using Λ-quantiles and applying results from robust quantiles. result Robust Λ-quantiles equal to Λ-quantiles of extremal distributions. SCQRNN prevents quantile crossing and improves computational efficiency.
problem Quantile crossing issue in regression models.
method Integrates ad hoc sorting in training to prevent quantile crossing and enhance computational efficiency.
result SCQRNN achieves faster convergence and non-intersecting quantiles.
Axiomatizes Λ-quantiles, a generalization of quantiles.
problem Found an axiomatization for Λ-quantiles. method Characterized Λ-quantiles using the locality property. result Local changes in distribution do not affect Λ-quantiles. Develops a method to ensure accurate quantile forecasts across multiple levels.
problem Ensuring accurate quantile forecasts at multiple levels, even under distribution shifts.
method Multi-level quantile tracker (MultiQT) wraps around any forecaster to produce calibrated forecasts.
result Guaranteed calibration of quantile forecasts at multiple levels, even against adversarial shifts.
Proposes a method to estimate conditional quantiles using both high-fidelity and low-fidelity data.
problem Difficulty in estimating conditional quantiles with scarce high-fidelity data.
method Two-stage, model-agnostic method using local quantile link and level function estimation.
result The method yields more accurate quantile estimates and tighter prediction intervals.
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 …
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.
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method co…