This paper proposes a method to automatically infer the quantile parameter in machine learning.
problem Estimating the quantile parameter in asymmetric loss functions.
method Jointly infers the quantile parameter and function parameters using convexity properties and a gradient boosting algorithm.
result The proposed method can automatically recover the quantile parameter and improve function parameter recovery.
Paper introduces a new robust loss function for RL.
problem Heuristic selection of threshold parameters in quantile Huber loss.
method Derived from Wasserstein distance, captures noise in quantile values.
result Enhances robustness against outliers and enables parameter adjustment.
Develops quantile diffusions for risk analysis in continuous time.
problem Stochastic dynamics of quantiles in continuous time.
method Construction of quantile processes through composite maps of distribution and quantile functions.
result Powerful method for interpreting quantile process characteristics in terms of model parameters.
The study analyzes the accuracy of quantile estimators in risk assessment using tail models.
problem Accurately assessing high quantiles in risk management with unknown distributions and sparse data.
method Used generalized Pareto distribution to model tail risks and calculated quantiles with finite sample bias and variance analysis.
result Determined the finite sample distribution function and bias/variance of quantile estimators.
IQ-BART models conditional quantiles using a non-parametric Bayesian approach.
problem Capturing multimodal predictive distributions in time series forecasting.
method Implicit Quantile BART (IQ-BART) augments data with quantile values for non-parametric quantile function estimation.
result IQ-BART provides flexible distribution-free regression with theoretical guarantees.
Quantile regression undercovers true uncertainty, revealing a bias in high dimensions.
problem Under-coverage bias in uncertainty estimation by quantile regression.
method Theoretical study on coverage of uncertainty estimation algorithms in learning quantiles.
result Quantile regression undercovers true uncertainty, revealing a bias in high dimensions.
A new method calculates risk loadings in classification ratemaking without subjective parameters.
problem Subjective risk loading parameters in classification ratemaking.
method Bootstrap method to calculate total risk premium, then determine risk loading parameters using quantile regression models.
result Risk premiums calculated by the new method reasonably differentiate different risk classes.
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…
Two novel procedures track quantiles efficiently using an oracle.
problem Setting step size and tuning parameters for incremental quantile estimators.
method Estimate MSE, decompose into variance and bias, use oracle to select best estimator.
result Efficient quantile tracking with error close to theoretical optimum.
Ensemble quantile classifier improves performance on high-dimensional data.
problem Discriminating high-dimensional data with heavy-tailed or skewed inputs.
method Regularized quantile classifier that assigns variable weights.
result Consistently estimates minimal population loss and is Bayes optimal.
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.
Localized debiased machine learning simplifies estimating quantile treatment effects.
problem Estimating quantile treatment effects in causal inference with many covariates and flexible relationships.
method Localized debiased machine learning (LDML) avoids learning the full nuisance function by estimating only at a single initial guess.
result LDML enables practically-feasible and theoretically-grounded efficient estimation of quantile treatment effects.
A new framework models quantile and Expected Shortfall simultaneously.
problem Modeling quantile and Expected Shortfall for risk assessment.
method Strictly consistent loss function for quantile and ES estimation, M- and Z-estimation.
result Consistency and asymptotic normality of estimators under weak conditions.
New framework calibrates computer models using deep learning and quantile regression.
problem Uncertainty in computer model input parameters due to high-dimensional time series data.
method Deep neural network with long-short term memory layers for inverse modeling, quantile regression for interval predictions.
result Accurate point and interval estimates for input parameters in WRF-hydro model.
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.
Paper tackles high-dimensional quantile regression with distribution shift using transfer learning.
problem Efficiency of knowledge transfer is severely impacted by distribution shift in high-dimensional regression.
method Proposes a novel transferable set and framework for three types of distribution shift: parameter, covariate, and residual.
result Establishes estimation error bounds and source detection consistency for the proposed method.
Improves random forest quantile estimation and prediction intervals.
problem Excessive bias in quantile estimates from random forests.
method Minimizes quantile coverage loss (QCL) by adjusting RF parameters.
result QCL-tuned RFs produce more accurate and narrower prediction intervals.
Introduces a new quantile regression method for financial and wage data analysis.
problem Analyzing expected shortfalls and wage inequality in financial and wage data.
method Weighted-average quantile regression framework and estimator.
result The estimator is T \sqrt T T -consistent and asymptotically normal. 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.
Improved asset allocation strategies using penalized quantile regression.
problem Improving investment strategies in asset allocation.
method Post-penalization, nonconvex penalties, and optimal tuning parameter selection.
result Alternative methods outperform simple LASSO, especially for extreme risk.
Bayesian method improves extreme quantile estimation with zero coverage error.
problem Estimating extreme quantiles with zero coverage error in small samples.
method Bayesian quantile estimation using Jeffreys prior.
result Bayesian method results in zero coverage error, unlike maximum likelihood.
Proposes robust local scaling using conditional quantiles of graph similarities.
problem Spectral analysis sensitivity to parameters and noise.
method Auto-encoding neural network for inferring conditional quantiles of similarity functions.
result Proposed approach outperforms existing methods in spectral clustering and single-example label propagation.
A method for estimating conditional mode using multiple quantile regressions.
problem Estimation of conditional mode with high-dimensional conditioning variables.
method Estimate conditional density by solving multiple quantile regressions, then find the maximum of the estimated density.
result The proposed method is computationally stable and statistically efficient with a fast convergence rate.
KQSP method prevents crossing issues in probabilistic K-line forecasts.
problem Quantile and K-line crossing issues in probabilistic K-line forecasts.
method Parameter-free and training-free reconciliation method (KQSP).
result KQSP reduces crossing rates to zero for all test data.
GBC methods compute expected utility without needing the model's density.
problem Computing expected utility in complex models.
method Density-free generative method using quantile neural estimator.
result Efficient estimation of expected utility from simulated data.
SUQUAN optimizes quantile normalisation for better downstream analysis.
problem Optimizing target distribution for better downstream analysis.
method Optimizes target distribution jointly with other parameters in the analysis.
result SUQUAN outperforms standard quantile normalisation on various data types.
We develop a method for quantile-based sensitivity analysis in models with discontinuities.
problem Uncertainty in interpreting discontinuous models using traditional derivatives.
method Quantile-based derivatives for discontinuous models with discrete inputs.
result Derivatives of quantile-based outputs are well-defined and provide meaningful insights.
This paper improves reinforcement learning by estimating return distributions using quantiles.
problem Improving reinforcement learning by estimating return distributions.
method Quantile-based distributional reinforcement learning, using quantile-projected distributional Bellman equations.
result The quantile-based approach achieves optimal sample efficiency and asymptotic efficiency.
This paper improves reinforcement learning by estimating return distributions using quantiles.
problem Improving reinforcement learning by estimating return distributions.
method The paper uses quantile-based distributional reinforcement learning to characterize return distributions.
result The quantile-based approach achieves optimal sample efficiency and asymptotic efficiency.
Bayesian QFSTS model tackles feature selection in quantile time series analysis.
problem Quantile feature selection in correlated multivariate time series data.
method Bayesian dimension reduction methodology using QFSTS model with multivariate asymmetric Laplace distribution, spike-and-slab prior, Metropolis-Hastings algorithm, and Bayesian model averaging.
result QFSTS model outperforms in feature selection, parameter estimation, and forecasting.
New Bayesian models optimize quantiles and expectiles for stochastic functions.
problem Optimizing for quantiles and expectiles in stochastic functions.
method Proposed variational models and BO strategies for quantile and expectile regression.
result Proposed models and strategies outperform existing methods in heteroscedastic, non-Gaussian settings.
The paper provides rigorous guarantees for m-out-of-n bootstrap estimators of sample quantiles.
problem Lack of parameter-free guarantees for robust inference with heavy-tailed data.
method Central limit theorem and Edgeworth expansion for m-out-of-n bootstrap estimators of sample quantiles.
result Established rigorous guarantees for the soundness of m-out-of-n bootstrap estimators of sample quantiles.
This paper analyzes convergence of DP-SGD with adaptive quantile clipping.
problem Empirical success of adaptive clipping methods lacks theoretical understanding.
method Comprehensive convergence analysis of SGD with quantile clipping (QC-SGD).
result Establishes theoretical guarantees for DP-QC-SGD, revealing relationships between quantile selection, step size, and convergence.
New model predicts financial transaction durations using quantiles.
problem Modeling financial transaction durations using traditional mean duration.
method Proposes a new autoregressive conditional duration model based on log-symmetric distributions reparametrized by quantiles.
result Proposed model allows for modeling different percentiles of financial transaction durations.
Optimal inference in distributed quantile regression without stringent scaling conditions.
problem Challenges in achieving optimal inference in distributed quantile regression due to the non-smooth nature of the QR loss function.
method Double-smoothing approach applied to local and global objective functions, with a trade-off between communication cost and statistical error.
result Established a finite-sample theoretical framework for distributed QR estimators, showing a trade-off between communication cost and statistical error.
Develops a model to forecast quantile-function-valued daily returns.
problem Summarizing and forecasting the distributional characteristics of intra-daily returns.
method Dynamic quantile function (DQF) model using Bayesian inference and MCMC algorithm.
result The DQF model outperforms other models in forecasting VaR of intra-daily returns.
Typically, operational risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for operational risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the P…
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.
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.
Paper develops robust estimation for loss models using L-moments.
problem Estimating parameters of loss models in non-life insurance.
method Develops novel, efficient and robust estimation procedure based on L-moments.
result Estimation method is more robust and efficient than current methods.
Paper analyzes statistical properties of log-cosh loss function.
problem No statistical analysis of log-cosh loss function in literature.
method Presented statistical properties of log-cosh loss function, compared to Cauchy distribution, and examined various statistical procedures.
result Characterized statistical properties of log-cosh loss function, including distribution, likelihood function, and Fisher information.
Bayesian approach models nonignorable missing data using copulas and marginal quantiles.
problem Nonignorable missing data in lead exposure and test score analysis.
method Gaussian copula model with auxiliary marginal quantiles for missingness indicators and study variables.
result Efficient MCMC algorithm estimates copula correlation and marginal distributions consistently.
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.
We propose a robust inferential procedure for assessing uncertainties of parameter estimation in high-dimensional linear models, where the dimension p p p can grow exponentially fast with the sample size n n n . Our method combines the de-biasing technique with the composite quantile function to construct an estimator that …
New framework robustifies loss functions with quantiles for outlier resistance.
problem Widespread outliers in big data affect statistical estimation and inference.
method Introduces a framework connecting to trimming, scalable algorithms, and new techniques.
result Robust estimators achieve minimax rate optimality in regression, classification, and neural networks.
This paper proposes a method to evaluate policies using quantile metrics, improving upon existing mean-based approaches.
problem Evaluating policies using mean-based metrics ignores the variability of outcomes, especially in skewed reward distributions.
method The paper introduces a doubly-robust inference procedure for quantile off-policy evaluation using deep conditional generative learning.
result The proposed estimator outperforms classical OPE estimators for mean outcomes in heavy-tailed reward distributions.
AI measures financial risk using linear quantile lasso regression.
problem Measuring systemic financial risk accurately and quantitatively.
method Linear quantile lasso regression with penalization parameter lambda.
result The Financial Risk Meter (FRM) is a valid measure of systemic risk.
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.