Analysis of variance for functional data

 Content: Introduction  Functional Data  Motivating Functional Data Why Is Functional Data Analysis Needed?  Overview of the Book  Implementation of Methodologies  Options for Reading This Book  Nonparametric Smoothers for a Single Curve  Introduction  Local Polynomial Kernel Smoothing Regression Splines Smoothing Splines P-Splines  Reconstruction of Functional Data  Introduction  Reconstruction Methods Accuracy of LPK Reconstructions Accuracy of LPK Reconstruction in FLMs  Stochastic Processes  Introduction  Stochastic Processes x2-Type Mixtures  F-Type Mixtures  One-Sample Problem for Functional Data  ANOVA for Functional Data  Introduction  Two-Sample Problem One-Way ANOVA Two-Way ANOVA  Linear Models with Functional Responses  Introduction  Linear Models with Time-Independent Covariates Linear Models with Time-Dependent Covariates  Ill-Conditioned Functional Linear Models  Introduction  Generalized Inverse Method Reparameterization Method Side-Condition Method  Diagnostics of Functional Observations  Introduction  Residual Functions Functional Outlier Detection Influential Case Detection  Robust Estimation of Coefficient Functions  Outlier Detection for a Sample of Functions  Heteroscedastic ANOVA for Functional Data  Introduction  Two-Sample Behrens-Fisher Problems Heteroscedastic One-Way ANOVA Heteroscedastic Two-Way ANOVA  Test of Equality of Covariance Functions  Introduction Two-Sample Case Multi-Sample Case  Bibliography  Index Technical Proofs, Concluding Remarks, Bibliographical Notes, and Exercises appear at the end of most chapters.
 Abstract:             \"Preface Functional data analysis has been a popular statistical research topic for the last three decades. Functional data are often obtained via observing a number of subjects over time, space or other continua densely. They are frequently collected from various research areas, including audiology, biology, children\'s growth studies, ergonomics, environmentology, me- teorology, and women\'s health studies among others in the form of curves, surfaces, or other complex objects. Statistical inference for functional data generally refers to estimation and hypothesis testing about functional data