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Inference for Functional Data with Applications (Hardcover, 2012 ed.)
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Inference for Functional Data with Applications (Hardcover, 2012 ed.)
Series: Springer Series in Statistics, 200
Expected to ship within 9 - 15 working days
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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.
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