Feb 5, 2024 · In this paper, we focus on linear quantile regression applied to “ultra-large” datasets, such as U.S. decennial censuses. A fast inference framework is ...

Jun 17, 2024 · In this paper, we develop a sequential algorithm, SQR, to support efficient quantile regression (QR) analysis for stream data.

Jul 31, 2024 · Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects ...

Nov 20, 2023 · This new calculation, the mosaic quantile regression, uses historic data to estimate the relationship between extreme outcomes of net load uncertainty, and load ...

Dec 12, 2023 · We introduce a new class of attacks based on performing quantile regression on the distribution of confidence scores induced by the model under attack on points ...

Oct 12, 2023 · To extend the capability of CQR with large-scale data, in this paper we propose a convolution-smoothed CQR (SCQR) method, complemented with iteratively ...

Jan 5, 2024 · The most commonly used QR framework is the conditional quantile regression (CQR). It is used to assess the impact of a covariate on a quantile of the outcome ...

Jul 31, 2024 · Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects ...

Feb 6, 2024 · The quantile regression model uses a time-varying regression coefficient function to quantify the association between covariates and the quantile level of ...

Sep 23, 2024 · In this paper, we propose Quantile Sub-Ensembles, a novel method to estimate uncertainty with ensemble of quantile-regression-based task networks and then ...