Joint Quantile Regression through Bayesian

We introduce a Bayesian semiparametric methodology for joint quantile regression with linearity and piecewise linearity constraints. We develop a probability model for all quantile curves in a continuum that define a coherent sampling distribution of the response variable. We provide a detailed illu...

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Bibliographic Details
Main Authors: Surya T Tokdar, Joseph B Kadane
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Bayesian inference
Gaussian process
Tropical cyclones
Change points
Markov chain Monte Carlo
North Atlantic
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.2304
http://www.stat.cmu.edu/cmu-stats/tr/tr876/tr876.pdf
Description
Summary:We introduce a Bayesian semiparametric methodology for joint quantile regression with linearity and piecewise linearity constraints. We develop a probability model for all quantile curves in a continuum that define a coherent sampling distribution of the response variable. We provide a detailed illustration of model fitting and inference by analyzing wind speed trends of tropical cyclones in the North Atlantic.

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