Abstract #302379

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JSM 2003 Abstract #302379
Activity Number: 316
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: General Methodology
Abstract - #302379
Title: Kernel-Based Bayesian Regression and Classification
Author(s): Bani K. Mallick*+ and Malay Ghosh
Companies: Texas A&M University and University of Florida
Address: Statistics, College Station, TX, 77843-3143,
Keywords:
Abstract:

We propose nonlinear Bayesian regression and classification models based on reproducing kernel Hilbert spaces (RKHS). We consider the Gaussian likelihood, logistic likelihood, and likelihoods related to the Support Vector Machine (SVM) models. These models are specifically useful when sample sizes are substantially smaller than the number of available predictor variables. We develop MCMC based computation methods and use them to perform posterior and predictive inferences. The regression problem is motivated by calibration problems in near-infrared (NIR) spectroscopy. We consider the nonlinear regression setting in which many predictor variables arise from sampling an essentially continuous curve at equally spaced points, and there may be multiple predictands. We will also use our nonlinear models to perform classification in popular datasets and in the analysis of microarray data.

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