This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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36
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Type:
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Contributed
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Date/Time:
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Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #309468 |
Title:
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Joint Quantile Regression: A Bayesian Approach
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Author(s):
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Surya Tokdar*+
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Companies:
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Duke University
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Address:
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426 Euphoria Cir, Cary, NC, 27519, United States
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Keywords:
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Bayesian inference ;
Quantile regression ;
Nonparametric models ;
Logistic Gaussian process ;
Tropical cyclones ;
Single index model
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Abstract:
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We consider a joint linear model for all conditional quantile curves of a response variable within a regression setting. For a single predictor the challenging monotonicity constraint is easily met through an interpolation of two monotone curves, modeled via logistic transformations of a Gaussian process. In analyzing time trends of north Atlantic tropical cyclone intensities, we demonstrate our joint model to offer significant improvement over a previously published analysis obtained by fitting separate quantile curves. By borrowing information across the entire conditional distribution, we conclude existence of upward trends of all conditional quantiles -- the previous analysis detected significant trends only in the upper tail. A multivariate extension is proposed and illustrated to offer richer inference than the standard linear heteroskedastic model in analyzing real-world data.
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