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Activity Number:
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548
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #304053 |
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Title:
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A Variable Selection Approach to Bayesian Monotonic Regression with Bernstein Polynomials
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Author(s):
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S. McKay Curtis*+ and Sujit Ghosh
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Companies:
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University of Washington and North Carolina State University
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Address:
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Department of Statistics, Seattle, WA, 98195-4320,
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Keywords:
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Markov chain Monte Carlo ; Stochastic Search
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Abstract:
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One of the standard problems in statistics consists of determining the relationship between a response variable and a single predictor variable through a regression function. Prior scientific knowledge is often available that suggests the regression function should have a certain shape (e.g. monotonically increasing or concave) but not necessarily a specific parametric form. Recently, Bernstein polynomials have been used to impose certain shape restrictions on regression functions. In this work, we demonstrate a connection between the monotonic regression problem and the variable selection problem in the linear model. We develop a Bayesian procedure for fitting the monotonic regression model by adapting currently available variable selection procedures. We demonstrate the effectiveness of our method through simulations and the analysis of real data.
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