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＜電子ブック＞
Inference for Functional Data with Applications / by Lajos Horváth, Piotr Kokoszka
(Springer Series in Statistics. ISSN:2197568X ; 200)

版	1st ed. 2012.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2012
本文言語	英語
大きさ	XIV, 422 p : online resource
著者標目	*Horváth, Lajos author Kokoszka, Piotr author SpringerLink (Online service)
件　名	LCSH:Statistics  FREE:Statistical Theory and Methods FREE:Statistics FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences FREE:Statistics in Business, Management, Economics, Finance, Insurance
一般注記	Independent functional observations -- The functional linear model -- Dependent functional data -- References -- Index This book presents recently developed statistical methods and theory required for the application of the tools of functional data analysis to problems arising in geosciences, finance, economics and biology. It is concerned with inference based on second order statistics, especially those related to the functional principal component analysis. While it covers inference for independent and identically distributed functional data, its distinguishing feature is an in depth coverage of dependent functional data structures, including functional time series and spatially indexed functions. Specific inferential problems studied include two sample inference, change point analysis, tests for dependence in data and model residuals and functional prediction. All procedures are described algorithmically, illustrated on simulated and real data sets, and supported by a complete asymptotic theory. The book can be read at two levels. Readers interested primarily in methodology will find detailed descriptions of the methods and examples of their application. Researchers interested also in mathematical foundations will find carefully developed theory. The organization of the chapters makes it easy for the reader to choose an appropriate focus. The book introduces the requisite, and frequently used, Hilbert space formalism in a systematic manner. This will be useful to graduate or advanced undergraduate students seeking a self-contained introduction to the subject. Advanced researchers will find novel asymptotic arguments HTTP:URL=https://doi.org/10.1007/978-1-4614-3655-3

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