03026aam a2200229 4500999001900000008004500019020001800064082001600082100002800098245004500126260002900171300002700200440005900227504043200286520172800718650003202446650004802478650004302526700004102569942001202610952017402622 c212008d212008190501b 2017 ||||| |||| 00| 0 eng d a9781498746342 a515.7bK6I6 aKokoszka, Piotr9379330 aIntroduction to functional data analysis bCRC Pressc2017aFlorida axvi, 290p.bWith index aChapman & Hall/CRC texts in statistical series9379331 aTable of Contents
1 First steps in the analysis of functional data
2 Further topics in exploratory FDA
3 Mathematical framework for functional data
4 Scalar- on - function regression
5 Functional response models
6 Functional generalized linear models
7 Sparse FDA
8 Functional time series
9 Spatial functional data and models
10 Elements of Hilbert space theory
11 Random functions
12 Inference from a random sample aIntroduction 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.
https://www.crcpress.com/Introduction-to-Functional-Data-Analysis/Kokoszka-Reimherr/p/book/9781498746342 aFunctional analysis9379332 aProbability theory and applications9379333 aStatistical theory and methods9379334 aReimherr, MattheweCo author9379335 2ddccBK 00102ddc406515_700000000000000_K6I6708NFIC9358380aVSLbVSLcGENd2019-04-30e12g4.00kSlot 1365 (0 Floor, East Wing)o515.7 K6I6p199254r2019-04-30v5830.64yBK