Description
Analysis of Variance for Functional Data
Chapman & Hall/CRC Monographs on Statistics and Applied Probability Series
Author: Zhang Jin-Ting
Language: EnglishSubjects for Analysis of Variance for Functional Data:
Keywords
Functional ANOVA; Wishart Process; hypothesis testing methods for functional data analysis; Nonparametric Bootstrap Tests; Bootstrap Test; functional hypothesis testing; Sample Covariance Function; nonparametric techniques for reconstructing functional data; Functional Data Set; functional linear models; NOx Emission Level; bootstrap tests for homogeneous and heteroscedastic two-sample problems; Approximate Null Distribution; diagnostics of functional observations; Functional Samples; testing equality of covariance functions; Null Distribution; pointwise; L2-norm-based; F-type; and bootstrap tests; Data Set; Functional Data; Smoothing Parameter; Regression Spline; Design Time Points; GCV Score; Ergonomics Data; Behrens Fisher Problem; Covariance Function; Gaussian Assumption; Nonparametric Smoothing Techniques; Naive Method; Random Permutation Test; Functional Outlier; Generalized Cook’s Distance
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Description
/li>Contents
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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® 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.
Introduction. Nonparametric Smoothers for a Single Curve. Reconstruction of Functional Data. Stochastic Processes. ANOVA for Functional Data. Linear Models with Functional Responses. Ill-Conditioned Functional Linear Models. Diagnostics of Functional Observations. Heteroscedastic ANOVA for Functional Data. Test of Equality of Covariance Functions. Bibliography. Index.
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.