Abstract
In this chapter we consider an infinite-dimensional generalization of Euclidean space introduced by the mathematician David Hilbert. This generalization preserves two fundamental geometric notions of Euclidean space— namely, distance and perpendicularity. Both of these geometric properties depend on the existence of an inner product. In the infinite-dimensional case, however, we take the inner product of functions rather than of vectors. Our emphasis here will be on concrete examples of Hilbert spaces relevant to statistics. To keep our discussion within bounds, some theoretical facts are stated without proof. Relevant proofs can be found in almost any book on real or functional analysis [6, 12]. Applications of our examples to numerical integration, wavelets, and other topics appear in later chapters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aronszajn N (1950) Theory of reproducing kernels. Amer Math Soc Trans 63:337-404
Berlinet A, Thomas-Agnan C (2004) Reproducing Kernel Hilbert Spaces in Probability and Statistics. Kluwer, Boston, MA
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2:121-167
Dym H, McKean HP (1972) Fourier Series and Integrals. Academic Press, New York
Hastie T, Tibshirani R, Friedman J (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York
Hewitt E, Stromberg K (1965) Real and Abstract Analysis. Springer, New York
Hochstadt H (1986) The Functions of Mathematical Physics. Dover, New York
Ismail MEH (2005) Classical and Quantum Orthogonal Polynomials in One Variable. Cambridge University Press, Cambridge
Newton HJ (2002) A Conversation with Emanuel Parzen. Stat Science 17:357-378
Parthasarathy KR (1977) Introduction to Probability and Measure. Springer, New York
Pearce ND, Wand MP (2006) Penalised splines and reproducing kernel methods. Amer Statistician 60:233-240
Rudin W (1973) Functional Analysis. McGraw-Hill, New York
Schölkopf B, Smola AJ (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA
Vapnik V (1995) The Nature of Statistical Learning Theory. Springer, New York
Wahba G (1990) Spline Models for Observational Data. SIAM, Philadelphia
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2010 Springer New York
About this chapter
Cite this chapter
Lange, K. (2010). Concrete Hilbert Spaces. In: Numerical Analysis for Statisticians. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5945-4_17
Download citation
DOI: https://doi.org/10.1007/978-1-4419-5945-4_17
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-5944-7
Online ISBN: 978-1-4419-5945-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)