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Introduction to Functional Data Analysis provides a concise
textbook introduction to the field. It explains how to analyze
functional data, both at exploratory and inferential levels. It
also provides a systematic and accessible exposition of the
methodology and the required mathematical framework. The book can
be used as textbook for a semester-long course on FDA for advanced
undergraduate or MS statistics majors, as well as for MS and PhD
students in other disciplines, including applied mathematics,
environmental science, public health, medical research, geophysical
sciences and economics. It can also be used for self-study and as a
reference for researchers in those fields who wish to acquire solid
understanding of FDA methodology and practical guidance for its
implementation. Each chapter contains plentiful examples of
relevant R code and theoretical and data analytic problems. The
material of the book can be roughly divided into four parts of
approximately equal length: 1) basic concepts and techniques of
FDA, 2) functional regression models, 3) sparse and dependent
functional data, and 4) introduction to the Hilbert space framework
of FDA. The book assumes advanced undergraduate background in
calculus, linear algebra, distributional probability theory,
foundations of statistical inference, and some familiarity with R
programming. Other required statistics background is provided in
scalar settings before the related functional concepts are
developed. Most chapters end with references to more advanced
research for those who wish to gain a more in-depth understanding
of a specific topic.
Introduction to Functional Data Analysis provides a concise
textbook introduction to the field. It explains how to analyze
functional data, both at exploratory and inferential levels. It
also provides a systematic and accessible exposition of the
methodology and the required mathematical framework. The book can
be used as textbook for a semester-long course on FDA for advanced
undergraduate or MS statistics majors, as well as for MS and PhD
students in other disciplines, including applied mathematics,
environmental science, public health, medical research, geophysical
sciences and economics. It can also be used for self-study and as a
reference for researchers in those fields who wish to acquire solid
understanding of FDA methodology and practical guidance for its
implementation. Each chapter contains plentiful examples of
relevant R code and theoretical and data analytic problems. The
material of the book can be roughly divided into four parts of
approximately equal length: 1) basic concepts and techniques of
FDA, 2) functional regression models, 3) sparse and dependent
functional data, and 4) introduction to the Hilbert space framework
of FDA. The book assumes advanced undergraduate background in
calculus, linear algebra, distributional probability theory,
foundations of statistical inference, and some familiarity with R
programming. Other required statistics background is provided in
scalar settings before the related functional concepts are
developed. Most chapters end with references to more advanced
research for those who wish to gain a more in-depth understanding
of a specific topic.
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