Jian Qing Shi, Taeryon Choi

Gaussian Process Regression Analysis for Functional Data (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Coveri

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Erscheinungstermin: 1. Juli 2011
• Englisch
• ISBN-13: 9781040070505
• Artikelnr.: 73153998

Autorenporträt

Jian Qing Shi, Ph.D., is a senior lecturer in statistics and the leader of the Applied Statistics and Probability Group at Newcastle University. He is a fellow of the Royal Statistical Society and associate editor of the Journal of the Royal Statistical Society (Series C). His research interests encompass functional data analysis using covariance kernel, incomplete data and model uncertainty, and covariance structural analysis and latent variable models.

Taeryon Choi, Ph.D., is an associate professor of statistics at Korea University. His research mainly focuses on the use of Bayesian methods and theory for various scientific problems.

Inhaltsangabe

Introduction. Bayesian Nonlinear Regression with Gaussian Process Priors.
Inference and Computation for Gaussian Process Regression Model. Covariance
Function and Model Selection. Functional Regression Analysis. Mixture
Models and Curve Clustering. Generalized Gaussian Process Regression for
Non-Gaussian Functional Data. Some Other Related Models. Appendices.
Bibliography. Index.