Bayesian inferenceB-splines LassoQuantile regressionRidgeSCADA number of nonstationary models have been developed to estimate extreme events as function of covariates. A quantile regression (QR) model is a stat
tidyfit is an R-package that facilitates and automates linear and nonlinear regression and classification modeling in a tidy environment. The package includes methods such as the Lasso, PLS, time-varying parameter or Bayesian model averaging regressions, and many more. The aim is threefold: Offer...
tidyfitis anR-package that facilitates and automates linear and nonlinear regression and classification modeling in a tidy environment. The package includes methods such as the Lasso, PLS, time-varying parameter or Bayesian model averaging regressions, and many more. The aim is threefold: Offer ast...
we propose to use Bayesian regularized quantile regression(BRQR)in the context of GP;the model has been successfully used in other research areas.We evaluated the prediction ability of the proposed model and compared it with the Bayesian ridge regression(BRR;equivalent to genomic best linear ...
Bayesian Information Criterion (BIC)Cross-validationKupiec testPinball scoreConditional predictive accuracyQuantile Regression Averaging (QRA) has sparked interest in the electricity price forecasting community after its unprecedented success in the Global Energy Forecasting Competition 2014, where the top two ...
Regularized Bayesian quantile regressionAsymmetric Laplace distributionBayesian inferenceB-splines LassoQuantile regressionRidgeSCADA number of nonstationary models have been developElUnivAdlouniUnivSalaheddineUnivSalaouUnivGarbaUnivSt-HilaireUnivAndreUniv
And based on the asymmetric Laplace distribution, the Bayesian regularized quantile regression approach performs better than the non-Bayesian approach in parameter estimation and prediction. Through real data analyses, we also confirm the above conclusions.应用数学与应用物理(英文)QiaoqiaoTangHaominZhang...
in this paper, we propose new Bayesian hierarchical representations of lasso, adaptive lasso and elastic net quantile regression models. We explore these representations by observing that the lasso penalty function corresponds to a scale mixture of truncated normal distribution (with exponential mixing ...
we study the Bayesian composite quantile regression with adaptive group Lasso penalty.The distinguishing characteristic of the newly proposed method is completely data adaptive without requiring prior knowledge of the error distribution.Extensive simulations and two real data examples are used to examine ...
Regularized Bayesian Quantile Regressiondoi:10.1080/03610918.2017.1280830Salaheddine El AdlouniGarba SalaouAndre St Hilaire