Results 31 to 40 of about 9,896 (259)

Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data

Mathematics, 2023
Quantile regression models are remarkable structures for conducting regression analyses when the data are subject to missingness. Missing values occur because of various factors like missing completely at random, missing at random, or missing not at ...
Xiaoning Li, Mulati Tuerde, Xijian Hu
doaj   +1 more source

Penalized Flexible Bayesian Quantile Regression

Applied Mathematics, 2012
The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper, we propose a flexible Bayesian Lasso and adaptive Lasso quantile regression by introducing a hierarchical model framework approach to ...
Yu, K, Alkenani, A, Alhamzawi, R
openaire   +4 more sources

The effect of agricultural futures price changes on the agricultural production in AP Vojvodina [PDF]

Ekonomika Poljoprivrede (1979), 2019
This paper investigates whether global agricultural futures of corn, wheat, oats, soybean and canola have any influence on the annual agricultural production of these plants in AP Vojvodina.
Živkov Dejan, Papić-Blagojević Nataša, Lončar Sanja   +2 more
doaj   +3 more sources

Performance Quantile Regression and Bayesian Quantile Regression in Dealing with Non-normal Errors (Case Study on Simulated Data)

Numerical: Jurnal Matematika dan Pendidikan Matematika
This research discusses the performance of quantile regression and Bayesian quantile regression methods. Quantile regression uses parameter estimation by maximizing the value of the likelihood function, while Bayesian quantile regression uses parameter ...
Lilis Harianti Hasibuan, Ferra Yanuar, Vika Pradinda Harahap, Latifatul Qalbi   +3 more
doaj   +1 more source

Regression Adjustment for Noncrossing Bayesian Quantile Regression [PDF]

Journal of Computational and Graphical Statistics, 2017
A two-stage approach is proposed to overcome the problem in quantile regression, where separately fitted curves for several quantiles may cross. The standard Bayesian quantile regression model is applied in the first stage, followed by a Gaussian process regression adjustment, which monotonizes the quantile function whilst borrowing strength from ...
Rodrigues, Thais, Fan, Yanan
openaire   +2 more sources

Deep Evidential Learning for Bayesian Quantile Regression

CoRR, 2023
It is desirable to have accurate uncertainty estimation from a single deterministic forward-pass model, as traditional methods for uncertainty quantification are computationally expensive. However, this is difficult because single forward-pass models do not sample weights during inference and often make assumptions about the target distribution, such ...
Frederik Boe Hüttel, Filipe Rodrigues 0001, Francisco Câmara Pereira   +2 more
openaire   +2 more sources

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source

ANALISIS PERTAMBAHAN TINGGI BADAN BALITA STUNTING DI PROVINSI SUMATERA BARAT MENGGUNAKAN METODE REGRESI KUANTIL BINER BAYESIAN

Jurnal Lebesgue
Stunting is one of the national health problems in Indonesia, where children experience growth failure. This study aims to construct a model for the classification of height gain of stunting toddlers in West Sumatra Province using the Bayesian binary ...
Cintya Mukti, Ferra Yanuar, Dodi Devianto   +2 more
doaj   +1 more source

Bayesian analysis for quantile smoothing spline

Statistical Theory and Related Fields, 2021
In Bayesian quantile smoothing spline [Thompson, P., Cai, Y., Moyeed, R., Reeve, D., & Stander, J. (2010). Bayesian nonparametric quantile regression using splines.
Zhongheng Cai, Dongchu Sun
doaj   +1 more source

Harnessing Large‐Scale Multi‐Omics Data for Risk Prediction and Deep Phenotyping of Valvular Heart Diseases in the General Population

Advanced Science, EarlyView.
Large‐scale UK Biobank analyses identify clinical and proteomic signatures for early prediction of valvular heart disease and its subtypes. Proteins add predictive value for VHD, AVS, and MVR, with outcome‐specific compact panels showing translational potential. Multi‐layer evidence highlights matrix remodeling, protease regulation, immune inflammation,
Zhihao Jiang, Yang Liu, Mingyu Song, Ning Chen, Canqing Yu, Jun Lv, Eric Yuk Fai Wan, Lu Qi, Liming Li, Dianjianyi Sun, Bang Zheng   +10 more
wiley   +1 more source