Regression Adjustment for Noncrossing Bayesian Quantile Regression
نویسندگان
چکیده
منابع مشابه
Noncrossing quantile regression curve estimation.
Since quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedur...
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1. Introduction: Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called Bayesian exponentially tilted empirical likelihood (BETEL), provides a likelihood for data y subject only to a set of m moment conditions of the form Eg(y, θ) = 0 where θ is a k dimensional parameter of interest and k may be smaller, equal to or larger than m. Th...
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Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ...
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Statistical Science) Bayesian Spatial Quantile Regression by Kristian Lum Department of Statistical Science Duke University
متن کاملBayesian Quantile Regression Methods∗
This 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. For regression quantiles we derive the asymptotic form of the posterior density. We also exami...
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2017
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2016.1172016