Results 41 to 50 of about 4,032,520 (72)
PhysicsâInformed Neural Networks for Battery Degradation Prediction Under Random Walk Operations
Quality and Reliability Engineering International, Volume 42, Issue 5, Page 1923-1940, July 2026.ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim +3 more
wiley +1 more source
Efficient quantile regression for heteroscedastic models [PDF]
, 2015 Quantile regression (QR) provides estimates of a range of conditional quantiles. This stands in contrast to traditional regression techniques, which focus on a single conditional mean function. Lee et al.
MacEachern, Steve N, +9 more
core +1 more source
AN ADAPTIVE COMPOSITE QUANTILE APPROACH TO DIMENSION REDUCTION [PDF]
, 2014 Sufficient dimension reduction [Li 1991] has long been a prominent issue in multivariate nonparametric regression analysis. To uncover the central dimension reduction space, we propose in this paper an adaptive composite quantile approach.
Kong, Efang
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New Bayesian Lasso Composite Quantile Regression [PDF]
, 2017 In this paper, we propose a new Bayesian lasso inference scheme for variable selection in composite quantile regression model (C Quantile Reg). The suggested approach is to construct a hierarchical structure within the Gibbs sampling under the assumption
Alhusseini, Fadel Hamid Hadi
core +1 more source
Bayesian Regularized Quantile Regression Using Adaptive Lasso and Its Applications
, 2022 This item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.Bayesian regularized procedures for parameter estimation ...
Chen, Ranran
core
Bayesian Iterative Adaptive Lasso Quantile Regression
IOSR Journal of Mathematics, 2017 Rahim Alhamzawi +1 more
openaire +1 more source
Bayesian adaptive Lasso quantile regression
Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective.
R. ALHAMZAWI, D. F. BENOIT, K. YU
core
Additive models for quantile regression: model selection and confidence bandaids [PDF]
Additive models for conditional quantile functions provide an attractive framework for nonparametric regression applications focused on features of the response beyond its central tendency.
Roger Koenker
core
Change point detection using Bayesian adaptive LASSO quantile regression
Computational StatisticsRanran Chen +4 more
openaire +1 more source
L1-Penalized quantile regression in high-dimensional sparse models [PDF]
We consider median regression and, more generally, quantile regression in high-dimensional sparse models. In these models the overall number of regressors p is very large, possibly larger than the sample size n, but only s of these regressors have non ...
Victor Chernozhukov, Alexandre Belloni
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