Results 11 to 20 of about 3,032,172 (354)
Local Quantile Regression [PDF]
Journal of Statistical Planning and Inference, 2010 Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables.
Härdle, Wolfgang Karl +2 more
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Factorisable Multitask Quantile Regression [PDF]
SSRN Electronic Journal, 2020 A multivariate quantile regression model with a factor structure is proposed to study data with many responses of interest. The factor structure is allowed to vary with the quantile levels, which makes our framework more flexible than the classical ...
Chao, Shih-Kang +2 more
core +3 more sources
Bayesian quantile regression [PDF]
Working Paper Series, 2005 Recent work by Schennach (2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment ...
Sung Jae Jun, Tony Lancaster
core +6 more sources
Sparse Quantile Regression [PDF]
Journal of Econometrics, 2020 We consider both $\ell _{0}$-penalized and $\ell _{0}$-constrained quantile regression estimators. For the $\ell _{0}$-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation ...
Lee, Sokbae (Simon), Chen, Le-Yu
openaire +3 more sources
Quantile Regression with Generated Regressors
Econometrics, 2021 This paper studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of
Liqiong Chen +2 more
doaj +1 more source
Desimal, 2023 The censored quantile regression method is a parameter estimation method that can be used to overcome censored data and BLUE (Best Linear Unbiased Estimator) assumptions that are not met.
Sarmada Sarmada +2 more
doaj +1 more source
Modified Quantile Regression for Modeling the Low Birth Weight
Frontiers in Applied Mathematics and Statistics, 2022 This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification.
Ferra Yanuar +2 more
doaj +1 more source
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for ...
Vilde Jensen, F. Bianchi, S. N. Anfinsen
semanticscholar +1 more source
A Bayesian Binary reciprocal LASSO quantile regression (with practical application)
Journal of Kufa for Mathematics and Computer, 2023 Quantile regression is one of the methods that has taken a wide space in application in the previous two decades because of the attractive features of these methods to researchers, as it is not affected by outliers values, meaning that it is considered ...
Mohammed Kahnger, Ahmad Naeem Flaih
doaj +1 more source
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2021 This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches.
Ferra Yanuar +4 more
doaj +1 more source