The book provides an introduction to functional data analysis (FDA), useful to students and researchers. FDA is now generally viewed as a fundamental subfield of statistics. FDA methods have been applied to science, business and engineering.
The book provides an introduction to functional data analysis (FDA), useful to students and researchers. FDA is now generally viewed as a fundamental subfield of statistics. FDA methods have been applied to science, business and engineering.
Piotr Kokoszka is a professor of statistics at Colorado State University. His research interests include functional data analysis, with emphasis on dependent data structures, and applications to geosciences and finance. He is a coauthor of the monograph Inference for Functional Data with Applications (with L. Horváth). He is an associate editor of several journals, including Computational Statistics and Data Analysis, Journal of Multivariate Analysis, Journal of Time Series Analysis, and Scandinavian Journal of Statistics. Matthew Reimherr is an assistant professor of statistics at Pennsylvania State University. His research interests include functional data analysis, with emphasis on longitudinal studies and applications to genetics and public health. He is an associate editor of Statistical Modeling.
Inhaltsangabe
First steps in the analysis of functional data Basis expansions Sample mean and covariance Principal component functions Analysis of BOA stock returns Diffusion tensor imaging Problems Further topics in exploratory FDA Derivatives Penalized smoothing Curve alignment Further reading Problems Mathematical framework for functional data Square integrable functions Random functions Linear transformations Scalar- on - function regression Examples Review of standard regression theory Difficulties specific to functional regression Estimation through a basis expansion Estimation with a roughness penalty Regression on functional principal components Implementation in the refund package Nonlinear scalar-on-function regression Problems Functional response models Least squares estimation and application to angular motion Penalized least squares estimation Functional regressors Penalized estimation in the refund package Estimation based on functional principal components Test of no effect Verification of the validity of a functional linear model Extensions and further reading Problems Functional generalized linear models Background Scalar-on-function GLM's Functional response GLM Implementation in the refund package Application to DTI Further reading Problems Sparse FDA Introduction Mean function estimation Covariance function estimation Sparse functional PCA Sparse functional regression Problems Functional time series Fundamental concepts of time series analysis Functional autoregressive process Forecasting with the Hyndman-Ullah method Forecasting with multivariate predictors Long-run covariance function Testing stationarity of functional time series Generation and estimation of the FAR(1) model using package fda Conditions for the existence of the FAR(1) process Further reading and other topics Problems Spatial functional data and models Fundamental concepts of spatial statistics Functional spatial fields Functional kriging Mean function estimation Implementation in the R package geofd Other topics and further reading Problems Elements of Hilbert space theory Hilbert space Projections and orthonormal sets Linear operators Basics of spectral theory Tensors Problems Random functions Random elements in metric spaces Expectation and covariance in a Hilbert space Gaussian functions and limit theorems Functional principal components Problems Inference from a random sample Consistency of sample mean and covariance functions Estimated functional principal components Asymptotic normality Hypothesis testing about the mean Confidence bands for the mean Application to BOA cumulative returns Proof of Theorem Problems
First steps in the analysis of functional data Basis expansions Sample mean and covariance Principal component functions Analysis of BOA stock returns Diffusion tensor imaging Problems Further topics in exploratory FDA Derivatives Penalized smoothing Curve alignment Further reading Problems Mathematical framework for functional data Square integrable functions Random functions Linear transformations Scalar- on - function regression Examples Review of standard regression theory Difficulties specific to functional regression Estimation through a basis expansion Estimation with a roughness penalty Regression on functional principal components Implementation in the refund package Nonlinear scalar-on-function regression Problems Functional response models Least squares estimation and application to angular motion Penalized least squares estimation Functional regressors Penalized estimation in the refund package Estimation based on functional principal components Test of no effect Verification of the validity of a functional linear model Extensions and further reading Problems Functional generalized linear models Background Scalar-on-function GLM's Functional response GLM Implementation in the refund package Application to DTI Further reading Problems Sparse FDA Introduction Mean function estimation Covariance function estimation Sparse functional PCA Sparse functional regression Problems Functional time series Fundamental concepts of time series analysis Functional autoregressive process Forecasting with the Hyndman-Ullah method Forecasting with multivariate predictors Long-run covariance function Testing stationarity of functional time series Generation and estimation of the FAR(1) model using package fda Conditions for the existence of the FAR(1) process Further reading and other topics Problems Spatial functional data and models Fundamental concepts of spatial statistics Functional spatial fields Functional kriging Mean function estimation Implementation in the R package geofd Other topics and further reading Problems Elements of Hilbert space theory Hilbert space Projections and orthonormal sets Linear operators Basics of spectral theory Tensors Problems Random functions Random elements in metric spaces Expectation and covariance in a Hilbert space Gaussian functions and limit theorems Functional principal components Problems Inference from a random sample Consistency of sample mean and covariance functions Estimated functional principal components Asymptotic normality Hypothesis testing about the mean Confidence bands for the mean Application to BOA cumulative returns Proof of Theorem Problems
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