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Informationen zum Autor Tailen Hsing Professor, Department of Statistics, University of Michigan, USA. Professor Hsing is a fellow of International Statistical Institute and of the Institute of Mathematical Statistics. He has published numerous papers on subjects ranging from bioinformatics to extreme value theory, functional data analysis, large sample theory and processes with long memory. Randall Eubank Professor Emeritus, School of Mathematical and Statistical Sciences, Arizona State University, USA. Professor Eubank is well know and respected in the functional data analysis (FDA) field. He has published numerous papers on the subject and is a regular invited speaker at key meetings. Klappentext Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators provides a uniquely broad compendium of the key mathematical concepts and results that are relevant for the theoretical development of functional data analysis (FDA).The self-contained treatment of selected topics of functional analysis and operator theory includes reproducing kernel Hilbert spaces, singular value decomposition of compact operators on Hilbert spaces and perturbation theory for both self-adjoint and non self-adjoint operators. The probabilistic foundation for FDA is described from the perspective of random elements in Hilbert spaces as well as from the viewpoint of continuous time stochastic processes. Nonparametric estimation approaches including kernel and regularized smoothing are also introduced. These tools are then used to investigate the properties of estimators for the mean element, covariance operators, principal components, regression function and canonical correlations. A general treatment of canonical correlations in Hilbert spaces naturally leads to FDA formulations of factor analysis, regression, MANOVA and discriminant analysis.This book will provide a valuable reference for statisticians and other researchers interested in developing or understanding the mathematical aspects of FDA. It is also suitable for a graduate level special topics course. Zusammenfassung ?? Provides a concise but rigorous account of the theoretical background of FDA. ?? Introduces topics in various areas of mathematics, probability and statistics from the perspective of FDA. ?? Presents a systematic exposition of the fundamental statistical issues in FDA. Inhaltsverzeichnis Preface xi 1 Introduction 1 1.1 Multivariate analysis in a nutshell 2 1.2 The path that lies ahead 13 2 Vector and function spaces 15 2.1 Metric spaces 16 2.2 Vector and normed spaces 20 2.3 Banach and L p spaces 26 2.4 Inner Product and Hilbert spaces 31 2.5 The projection theorem and orthogonal decomposition 38 2.6 Vector integrals 40 2.7 Reproducing kernel Hilbert spaces 46 2.8 Sobolev spaces 55 3 Linear operator and functionals 61 3.1 Operators 62 3.2 Linear functionals 66 3.3 Adjoint operator 71 3.4 Nonnegative, square-root, and projection operators 74 3.5 Operator inverses 77 3.6 Fréchet and Gâteaux derivatives 83 3.7 Generalized Gram-Schmidt decompositions 87 4 Compact operators and singular value decomposition 91 4.1 Compact operators 92 4.2 Eigenvalues of compact operators 96 4.3 The singular value decomposition 103 4.4 Hilbert-Schmidt operators 107 4.5 Trace class operators 113 4.6 Integral operators and Mercer's Theorem 116 4.7 Operators on an RKHS 123 4.8 Simultaneous diagonalization of two nonnegative definite operators 126 5 Perturbation theory 129 5.1 Perturbation of self-adjoint compact operators 129 5.2 Perturbation of general compact operators 140 6 Smoothing and regularization 147 <...
