Analysis of Variance for Functional Data

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A01=Jin-Ting Zhang
advanced functional data analysis methods
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and bootstrap tests
Approximate Null Distribution
Author_Jin-Ting Zhang
automatic-update
basis
Behrens Fisher Problem
Bootstrap Test
bootstrap tests for homogeneous and heteroscedastic two-sample problems
canadian
Category1=Non-Fiction
Category=PBT
Category=PS
COP=United Kingdom
covariance
Covariance Function
covariance function testing
Data Set
Delivery_Pre-order
design
Design Time Points
diagnostics of functional observations
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eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Ergonomics Data
F-type
Functional ANOVA
Functional Data
functional data modeling
Functional Data Set
functional hypothesis testing
functional linear models
Functional Outlier
Functional Samples
Gaussian Assumption
GCV Score
Generalized Cook's Distance
Generalized Cook’s Distance
hypothesis testing methods for functional data analysis
L2-norm-based
Language_English
MATLAB statistical programming
Naive Method
Nonparametric Bootstrap Tests
nonparametric smoothing
Nonparametric Smoothing Techniques
nonparametric techniques for reconstructing functional data
NOx Emission Level
Null Distribution
PA=Not yet available
pointwise
power
Price_€50 to €100
PS=Forthcoming
Random Permutation Test
regression
Regression Spline
Sample Covariance Function
set
Smoothing Parameter
softlaunch
spline
statistical hypothesis testing
stochastic processes analysis
temperature
testing equality of covariance functions
truncated
Wishart Process

Product details

  • ISBN 9781032920399
  • Weight: 760g
  • Dimensions: 156 x 234mm
  • Publication Date: 14 Oct 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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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.

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.

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