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Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression

dc.contributor.advisor Mukherjee, Sayan
dc.contributor.author Mao, Kai
dc.date.accessioned 2009-12-18T16:24:23Z
dc.date.available 2009-12-18T16:24:23Z
dc.date.issued 2009
dc.identifier.uri https://hdl.handle.net/10161/1581
dc.description.abstract <p>We propose nonparametric Bayesian models for supervised dimension</p><p>reduction and regression problems. Supervised dimension reduction is</p><p>a setting where one needs to reduce the dimensionality of the</p><p>predictors or find the dimension reduction subspace and lose little</p><p>or no predictive information. Our first method retrieves the</p><p>dimension reduction subspace in the inverse regression framework by</p><p>utilizing a dependent Dirichlet process that allows for natural</p><p>clustering for the data in terms of both the response and predictor</p><p>variables. Our second method is based on ideas from the gradient</p><p>learning framework and retrieves the dimension reduction subspace</p><p>through coherent nonparametric Bayesian kernel models. We also</p><p>discuss and provide a new rationalization of kernel regression based</p><p>on nonparametric Bayesian models allowing for direct and formal</p><p>inference on the uncertain regression functions. Our proposed models</p><p>apply for high dimensional cases where the number of variables far</p><p>exceed the sample size, and hold for both the classical setting of</p><p>Euclidean subspaces and the Riemannian setting where the marginal</p><p>distribution is concentrated on a manifold. Our Bayesian perspective</p><p>adds appropriate probabilistic and statistical frameworks that allow</p><p>for rich inference such as uncertainty estimation which is important</p><p>for measuring the estimates. Formal probabilistic models with</p><p>likelihoods and priors are given and efficient posterior sampling</p><p>can be obtained by Markov chain Monte Carlo methodologies,</p><p>particularly Gibbs sampling schemes. For the supervised dimension</p><p>reduction as the posterior draws are linear subspaces which are</p><p>points on a Grassmann manifold, we do the posterior inference with</p><p>respect to geodesics on the Grassmannian. The utility of our</p><p>approaches is illustrated on simulated and real examples.</p>
dc.format.extent 820771 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Statistics
dc.subject Dirichlet process
dc.subject Kernel models
dc.subject Nonparametric Bayesian
dc.subject Supervised dimension reduction
dc.title Nonparametric Bayesian Models for Supervised Dimension Reduction and Regression
dc.type Dissertation
dc.department Statistical Science


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