Gaussian Process Regression Analysis for Functional Data (PDF)
(Sprache: Englisch)
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...
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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.
Autoren-Porträt von Jian Qing Shi, Taeryon Choi
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.
Bibliographische Angaben
- Autoren: Jian Qing Shi , Taeryon Choi
- 2011, 216 Seiten, Englisch
- Verlag: Taylor & Francis
- ISBN-10: 1439837740
- ISBN-13: 9781439837740
- Erscheinungsdatum: 01.07.2011
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