Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo

被引:0
作者
Hachem, Hassan [1 ]
Abboud, Candy [2 ]
机构
[1] AgroParisTech, INRAE, UR1204, 147 Rue Univ, F-75338 Paris, France
[2] Amer Univ Middle East, Coll Engn & Technol, Egaila 54200, Kuwait
关键词
simultaneous quantile regression; Bayesian approach; Hamiltonian Monte Carlo; estimation; asymmetric Laplace distribution;
D O I
10.3390/a17060224
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The simultaneous estimation of multiple quantiles is a crucial statistical task that enables a thorough understanding of data distribution for robust analysis and decision-making. In this study, we adopt a Bayesian approach to tackle this critical task, employing the asymmetric Laplace distribution (ALD) as a flexible framework for quantile modeling. Our methodology implementation involves the Hamiltonian Monte Carlo (HMC) algorithm, building on the foundation laid in prior work, where the error term is assumed to follow an ALD. Capitalizing on the interplay between two distinct quantiles of this distribution, we endorse a straightforward and fully Bayesian method that adheres to the non-crossing property of quantiles. Illustrated through simulated scenarios, we showcase the effectiveness of our approach in quantile estimation, enhancing precision via the HMC algorithm. The proposed method proves versatile, finding application in finance, environmental science, healthcare, and manufacturing, and contributing to sustainable development goals by fostering innovation and enhancing decision-making in diverse fields.
引用
收藏
页数:18
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