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Generalised gaussian process functional regression model

Presented by: 
JQ Shi [Newcastle]
Date: 
Thursday 26th June 2008 - 14:20 to 14:40
Venue: 
INI Seminar Room 1
Session Chair: 
Doug Nychka
Abstract: 

In this talk, I will discuss a functional regression problem with non-Gaussian functional (longitudinal) response with functional predictors. This type of problem includes for example binomial and Poisson response data, occurring in many bi-medical and engineering experiments. We proposed a generalised Gaussian process functional regression model for such regression situation. We suppose that there exists an underlying latent process between the inputs and the response. The latent process is defined by Gaussian process functional regression model, which is connected with stepwise response data by means of a link function.

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons