Results 271 to 280 of about 10,357 (300)

Bayesian Semiparametric Modelling in Quantile Regression

Scandinavian Journal of Statistics, 2009
Abstract.  We propose a Bayesian semiparametric methodology for quantile regression modelling. In particular, working with parametric quantile regression functions, we develop Dirichlet process mixture models for the error distribution in an additive quantile regression formulation.
Kottas, Athanasios, Krnjajić, Milovan
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The Expectation–Maximization approach for Bayesian quantile regression

Computational Statistics & Data Analysis, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kaifeng Zhao 0001, Heng Lian
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Bayesian bridge-randomized penalized quantile regression

Computational Statistics & Data Analysis, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yuzhu Tian, Xinyuan Song 0001
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Bayesian Quantile Regression

2017
This chapter provides a review of Bayesian quantile regression methods based on different types of working likelihoods. It discusses some developments in Bayesian quantile regression approaches based on various working likelihoods, including parametric likelihood based on the asymmetric Laplace distribution, empirical likelihood, and some ...
Huixia Judy Wang, Yunwen Yang
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Variational Bayesian Tensor Quantile Regression

Acta Mathematica Sinica, English Series
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin, Yunzhi, Zhang, Yanqing
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Bayesian composite quantile regression

Journal of Statistical Computation and Simulation, 2015
One advantage of quantile regression, relative to the ordinary least-square (OLS) regression, is that the quantile regression estimates are more robust against outliers and non-normal errors in the response measurements. However, the relative efficiency of the quantile regression estimator with respect to the OLS estimator can be arbitrarily small.
Hanwen Huang, Zhongxue Chen
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Bayesian quantile regression methods

Journal of Applied Econometrics, 2010
AbstractThis paper is a study of the application of Bayesian exponentially tilted empirical likelihood to inference about quantile regressions. In the case of simple quantiles we show the exact form for the likelihood implied by this method and compare it with the Bayesian bootstrap and with Jeffreys' method.
Tony Lancaster, Sung Jae Jun
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Automatic Bayesian quantile regression curve fitting

Statistics and Computing, 2008
Quantile regression, including median regression, as a more completed statistical model than mean regression, is now well known with its wide spread applications. Bayesian inference on quantile regression or Bayesian quantile regression has attracted much interest recently.
Colin Chen, Keming Yu
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Bayesian time‐varying quantile regression to extremes

Environmetrics, 2019
AbstractMaximum analysis consists of modeling the maximums of a data set by considering a specific distribution. Extreme value theory (EVT) shows that, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution.
Fernando Ferraz Do Nascimento, Marcelo Bourguignon   +1 more
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Variational Inference for Nonparametric Bayesian Quantile Regression

Proceedings of the AAAI Conference on Artificial Intelligence, 2015
Quantile regression deals with the problem of computing robust estimators when the conditional mean and standard deviation of the predicted function are inadequate to capture its variability. The technique has an extensive list of applications, including health sciences, ecology and finance.
Sachinthaka Abeywardana, Fabio Ramos 0001   +1 more
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