Jian Qing Shi, Taeryon Choi

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

• Format: PDF

Produktbeschreibung

This work presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. They also explore novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Some MATLAB and C codes are available on the first author's website.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 216
• Erscheinungstermin: 1. Juli 2011
• Englisch
• ISBN-13: 9781439837740
• Artikelnr.: 39142490

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.