Results 31 to 40 of about 2,699,126 (275)
Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2018 The purposes of this paper is to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study.
Catrin Muharisa +2 more
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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
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Bayesian quantile regression for single-index models [PDF]
Statistics and Computing, 2012 Using an asymmetric Laplace distribution, which provides a mechanism for Bayesian inference of quantile regression models, we develop a fully Bayesian approach to fitting single-index models in conditional quantile regression. In this work, we use a Gaussian process prior for the unknown nonparametric link function and a Laplace distribution on the ...
Yuao Hu, Robert B. Gramacy, Heng Lian
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PLoS ONE, 2020 This paper presents a Bayesian analysis of linear mixed models for quantile regression based on a Cholesky decomposition for the covariance matrix of random effects.
Yonggang Ji, Haifang Shi
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Bayesian lasso binary quantile regression
Computational Statistics, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dries F. Benoit +2 more
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Quantile Regression Neural Networks: A Bayesian Approach [PDF]
Journal of Statistical Theory and Practice, 2021 This article introduces a Bayesian neural network estimation method for quantile regression assuming an asymmetric Laplace distribution (ALD) for the response variable. It is shown that the posterior distribution for feedforward neural network quantile regression is asymptotically consistent under a misspecified ALD model. This consistency proof embeds
S. R. Jantre, S. Bhattacharya, T. Maiti
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BayesianKAN: A reaction condition optimization framework integrating Kolmogorov-Arnold network and Bayesian optimization. [PDF]
Smart MolA novel BayesianKAN framework integrates Kolmogorov‐Arnold networks with Bayesian optimization to efficiently navigate complex factor spaces, accelerating the discovery of optimal reaction conditions for chemical synthesis. Abstract Efficient optimization of chemical reaction conditions is crucial for enhancing reaction yield and selectivity, yet ...
Wang J +5 more
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Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression
International Journal of Mathematics and Mathematical Sciences, 2021 Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of
Ezekiel N. N. Nortey +4 more
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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
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Prior elicitation in Bayesian quantile regression for longitudinal data [PDF]
, 2011 © 2011 Alhamzawi R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original auhor and source are ...
Alhamzawi, Rahim +7 more
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