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 l



Autorentext

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.



Inhalt

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.

Titel
Analysis of Variance for Functional Data
EAN
9781439862742
ISBN
978-1-4398-6274-2
Format
E-Book (pdf)
Herausgeber
Veröffentlichung
18.06.2013
Digitaler Kopierschutz
Adobe-DRM
Dateigrösse
4.66 MB
Anzahl Seiten
412
Jahr
2013
Untertitel
Englisch