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Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves and clustering.
List of contents
1. Basic Concepts. 2. Key Methodological Concepts. 3. Functional Principal Components Analysis. 4. Scalar-on-Function Regression. 5. Function-on-Scalar Regression. 6. Function-on-Function Regression. 7. Survival Analysis with Functional Predictors. 8. Multilevel Functional Data Analysis. 9. Clustering of Functional Data.
About the author
Ciprian M. Crainiceanu is Professor of Biostatistics at Johns Hopkins University working on wearable and implantable technology (WIT), signal processing, and clinical neuroimaging. He has extensive experience in mixed effects modeling, semiparametric regression, and functional data analysis with application to data generated by emerging technologies.
Jeff Goldsmith is Associate Dean for Data Science and Associate Professor of Biostatistics at the Columbia University Mailman School of Public Health. His work in functional data analysis includes methodological and computational advances with applications in reaching kinematics, wearable devices, and neuroimaging.
Andrew Leroux is an Assistant Professor of Biostatistics and Informatics at the University of Colorado. His interests include the development of methodology in functional data analysis, particularly related to wearable technologies and intensive longitudinal data.
Erjia Cui is an Assistant Professor of Biostatistics at the University of Minnesota. His research interests include developing functional data analysis methods and semiparametric regression models with reproducible software, with applications in wearable devices, mobile health, and imaging.
Summary
Functional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves and clustering.
