Abstract Details
Activity Number:
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252
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Type:
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Contributed
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Date/Time:
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Monday, August 5, 2013 : 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 - #307972 |
Title:
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Switching Nonparametric Regression Models
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Author(s):
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Camila De Souza*+ and Nancy Heckman
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Companies:
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University of British Columbia and University of British Columbia
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Keywords:
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Functional data analysis ;
Latent variables ;
EM algorithm ;
Nonparametric regression ;
Gaussian processes ;
Smoothing
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Abstract:
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We propose a methodology to analyze functional data arising from a curve that, over its domain, changes between J states. The state at any particular point is determined by a latent stochastic process. The state also determines a function.
We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z. The possible values of the states are j=1,...,J. If z=j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. In a Bayesian switching nonparametric regression model the J functions are realizations of stochastic processes (e.g., Gaussian processes). In a frequentist switching nonparametric regression model the J functions are merely assumed to be smooth.
We modify the EM algorithm to estimate the parameters from the latent state process and the functions corresponding to the J states. We also obtain standard errors for the parameter estimators of the state process. We conduct simulation studies and an application to a data set.
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Authors who are presenting talks have a * after their name.
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