Results 21 to 30 of about 9,896 (259)
Bayesian composite quantile regression for the single-index model.
PLoS ONE, 2023 By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model.
Xiaohui Yuan, Xuefei Xiang, Xinran Zhang
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A Bayesian hurdle quantile regression model for citation analysis with mass points at lower values
Quantitative Science Studies, 2021 Quantile regression presents a complete picture of the effects on the location, scale, and shape of the dependent variable at all points, not just the mean.
Marzieh Shahmandi +2 more
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Bayesian adaptive Lasso quantile regression [PDF]
Statistical Modelling, 2012 Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients.
R. ALHAMZAWI, K. YU, D. F. BENOIT
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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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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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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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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 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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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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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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