Analysis of Variance for Functional Data

Analysis of Variance for Functional Data presents up-to-date hypothesis testing methods for functional data analysis. It 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 and discusses pointwise, L2-norm-based, F-type, and bootstrap tests.

Assuming only basic knowledge of statistics, calculus, and matrix algebra, this book explains 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 as well as MATLAB codes for analyzing the data examples can be downloaded from the author’s website.