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Analysis of Variance for Functional Data

Jin-Ting Zhang (National University of Singapore)



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Taylor & Francis Inc
18 June 2013
Probability & statistics; Applied mathematics
Despite research interest in functional data analysis in the last three decades, few books are available on the subject. Filling this gap, Analysis of Variance for Functional Data presents up-to-date hypothesis testing methods for functional data analysis. The book covers the reconstruction of functional observations, functional ANOVA, functional linear models with functional responses, ill-conditioned functional linear models, diagnostics of functional observations, heteroscedastic ANOVA for functional data, and testing equality of covariance functions. Although the methodologies presented are designed for curve data, they can be extended to surface data.

Useful for statistical researchers and practitioners analyzing functional data, this self-contained book gives both a theoretical and applied treatment of functional data analysis supported by easy-to-use MATLAB (R) code. The author provides a number of simple methods for functional hypothesis testing. He discusses pointwise, L2-norm-based, F-type, and bootstrap tests.

Assuming only basic knowledge of statistics, calculus, and matrix algebra, the book explains the key ideas at a relatively low technical level using real data examples. Each chapter also includes bibliographical notes and exercises. Real functional data sets from the text and MATLAB codes for analyzing the data examples are available for download from the author's website.
By:   Jin-Ting Zhang (National University of Singapore)
Imprint:   Taylor & Francis Inc
Country of Publication:   United States
Volume:   127
Dimensions:   Height: 235mm,  Width: 159mm,  Spine: 15mm
Weight:   703g
ISBN:   9781439862735
ISBN 10:   1439862737
Series:   Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Pages:   410
Publication Date:   18 June 2013
Audience:   College/higher education ,  College/higher education ,  Professional and scholarly ,  Primary ,  Primary
Format:   Hardback
Publisher's Status:   Active

Jin-Ting Zhang is an associate professor in the Department of Statistics and Applied Probability at the National University of Singapore. He has published extensively and has served on the editorial boards of several international statistical journals. He is the coauthor of Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effect Modelling Approaches and the coeditor of Advances in Statistics: Proceedings of the Conference in Honor of Professor Zhidong Bai on His 65th Birthday.

Reviews for Analysis of Variance for Functional Data

... a focused presentation of functional ANOVA and linear function-on-scalar regression problems using the `smooth first' approach to estimation and inference. I would recommend this book to anyone interested in theoretical developments and hypothesis testing in this commonly encountered class of problems. -Jeff Goldsmith, Journal of the American Statistical Association, March 2014

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