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Introduction to Functional Data Analysis

Author: Piotr Kokoszka; Matthew Reimherr
Publisher: Boca Raton, FL : CRC Press, 2017.
Series: Texts in statistical science.
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:
"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,  Read more...
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Genre/Form: Electronic book
Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Piotr Kokoszka; Matthew Reimherr
ISBN: 9781315117416 131511741X 9781498746625 1498746624
OCLC Number: 993988864
Description: 1 online resource (xii, 290 pages).
Contents: Chapter 1 First steps in the analysis of functional data / Piotr Kokoszka --
chapter 2 Further topics in exploratory analysis of functional data / Piotr Kokoszka --
chapter 3 Mathematical framework for functional data / Piotr Kokoszka --
chapter 4 Scalar-on-function regression / Piotr Kokoszka --
chapter 5 Functional response models / Piotr Kokoszka --
chapter 6 Functional generalized linear models / Piotr Kokoszka --
chapter 7 Sparse FDA / Piotr Kokoszka --
chapter 8 Functional time series / Piotr Kokoszka --
chapter 9 Spatial functional data and models / Piotr Kokoszka --
chapter 10 Elements of Hilbert space theory / Piotr Kokoszka --
chapter 11 Random functions / Piotr Kokoszka --
chapter 12 Inference from a random sample / Piotr Kokoszka.
Series Title: Texts in statistical science.
Responsibility: Piotr Kokoszka.

Abstract:

"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."--Provided by publisher.

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"This well-written book provides a great and intuitive introduction to functional data analysis (FDA) which has emerged as an important area in statistics and found tons of scientific Read more...

 
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