Results 31 to 40 of about 10,357 (300)
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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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 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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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 nonparametric quantile process regression and estimation of marginal quantile effects [PDF]
Biometrics, 2021 AbstractFlexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy‐related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimate noncrossing, nonlinear quantile curves. We expand the conditional distribution function of the
Steven G. Xu, Brian J. Reich
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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
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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
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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 +2 more
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Numerical: Jurnal Matematika dan Pendidikan MatematikaThis 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 +3 more
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