A Bayesian Nonparametric Approach to Inference for Quantile Regression
We develop a Bayesian method for nonparametric model-based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for different quantile curves emerging from the conditional response...
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Veröffentlicht in: | Journal of business & economic statistics 2010-07, Vol.28 (3), p.357-369 |
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creator | Taddy, Matthew A. Kottas, Athanasios |
description | We develop a Bayesian method for nonparametric model-based quantile regression. The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for different quantile curves emerging from the conditional response distribution given the covariates. An extension to allow for partially observed responses leads to a novel Tobit quantile regression framework. We use simulated data sets and two data examples from the literature to illustrate the capacity of the model to uncover nonlinearities in quantile regression curves, as well as nonstandard features in the response distribution. |
doi_str_mv | 10.1198/jbes.2009.07331 |
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The approach involves flexible Dirichlet process mixture models for the joint distribution of the response and the covariates, with posterior inference for different quantile curves emerging from the conditional response distribution given the covariates. An extension to allow for partially observed responses leads to a novel Tobit quantile regression framework. We use simulated data sets and two data examples from the literature to illustrate the capacity of the model to uncover nonlinearities in quantile regression curves, as well as nonstandard features in the response distribution.</description><subject>Bayesian analysis</subject><subject>Children</subject><subject>Datasets</subject><subject>Dirichlet process mixture model</subject><subject>Economic models</subject><subject>Inference</subject><subject>Markov chain Monte Carlo</subject><subject>Modeling</subject><subject>Multivariate normal mixture</subject><subject>Nonparametric models</subject><subject>Parametric models</subject><subject>Quantile regression</subject><subject>Regression analysis</subject><subject>Simulation</subject><subject>Simulations</subject><subject>Studies</subject><subject>Tobit quantile regression</subject><issn>0735-0015</issn><issn>1537-2707</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><recordid>eNp1kEFLAzEQhYMoWKtnT0Lwvu1kt9nseqvFaqEoip5Dkp3olm1Sky3Sf29qxZtzGZj35s3wEXLJYMRYXY1XGuMoB6hHIIqCHZEB44XIcgHimAzSjGcAjJ-SsxhXkKri5YDMp_RW7TC2ytFH7zYqqDX2oTV0utkEr8wH7T1dOIsBnUFqfaDPW-X6tkP6gu8BY2y9OycnVnURL377kLzN715nD9ny6X4xmy4zMwHeZw3LrQbLSzQaRcV1rpkWrABWcpiA0oZxWzclYmUbzUprYKJNMqJQTc7rYkiuD7npt88txl6u_Da4dFJyKFJKIfam8cFkgo8xoJWb0K5V2EkGcs9K7lnJPSv5wyptXB02VrH34c-eg-BQTXjSbw566xKAtfryoWtkr3adDzYoZ9ooi__CvwFBTnqW</recordid><startdate>20100701</startdate><enddate>20100701</enddate><creator>Taddy, Matthew A.</creator><creator>Kottas, Athanasios</creator><general>Taylor & Francis</general><general>American Statistical Association</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20100701</creationdate><title>A Bayesian Nonparametric Approach to Inference for Quantile Regression</title><author>Taddy, Matthew A. ; Kottas, Athanasios</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c405t-d12fb0f56ecbe785b2b1b7130165040abc15f9d6ee8fdb16fc04bc785e7ad2593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Bayesian analysis</topic><topic>Children</topic><topic>Datasets</topic><topic>Dirichlet process mixture model</topic><topic>Economic models</topic><topic>Inference</topic><topic>Markov chain Monte Carlo</topic><topic>Modeling</topic><topic>Multivariate normal mixture</topic><topic>Nonparametric models</topic><topic>Parametric models</topic><topic>Quantile regression</topic><topic>Regression analysis</topic><topic>Simulation</topic><topic>Simulations</topic><topic>Studies</topic><topic>Tobit quantile regression</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taddy, Matthew A.</creatorcontrib><creatorcontrib>Kottas, Athanasios</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of business & economic statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taddy, Matthew A.</au><au>Kottas, Athanasios</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Bayesian Nonparametric Approach to Inference for Quantile Regression</atitle><jtitle>Journal of business & economic statistics</jtitle><date>2010-07-01</date><risdate>2010</risdate><volume>28</volume><issue>3</issue><spage>357</spage><epage>369</epage><pages>357-369</pages><issn>0735-0015</issn><eissn>1537-2707</eissn><abstract>We develop a Bayesian method for nonparametric model-based quantile regression. 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subjects | Bayesian analysis Children Datasets Dirichlet process mixture model Economic models Inference Markov chain Monte Carlo Modeling Multivariate normal mixture Nonparametric models Parametric models Quantile regression Regression analysis Simulation Simulations Studies Tobit quantile regression |
title | A Bayesian Nonparametric Approach to Inference for Quantile Regression |
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