A method to perform Bayesian function-on-scalar quantile regression, i.e., Bayesian FQR. The proposed method is applicable to data sets given the N by T matrix of response functions Y …

2020年2月9日 · In this article, we study the FQR model for densely sampled, high-dimensional functional data without relying on parametric error or independent stochastic process …

Functional quantile regression (FQR) is a useful alternative to mean regression for func-tional data as it provides a comprehensive understanding of how scalar predictors in uence the …

The fqr package makes quantile regression fast and scaleable using accelerated gradient descent. For both big and small problems it is substantially faster than other quantile …

We introduce a scalable, distributed strategy to perform FQR that can account for intrafunctional dependence structures in the functional responses. This general dis-

In this paper, we propose a robust fQR (called ‘RfPQR’) to robustly estimate the regression quantiles in the presence of both outliers and leverage points. Our proposed method is based …

2022年7月1日 · Functional quantile regression (fQR) models essentially extend the standard quantile regression framework to account for functional covariates: the effect of the covariate …

The Bayesian FQR model with and without adjustment and the bootstrap-based QR with wavelet denoising produced an estimate of B 2 0.9 (t) that are clearly greater in magnitude than B 2 …

使用Fqr控制算法能够实现快速、准确的过程控制，同时在不同的操作条件下，保持过程的可控性和稳定性。 这种算法在自动化控制系统，尤其是在工业环境中是非常重要的。

quantile regression (fQR) models essentially extend the standard quantile regression framework to account for func-tional covariates: the effect of the covariate on a particular quantile of the …