Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-26T12:29:07.975Z Has data issue: false hasContentIssue false

Fractal functional quantization of mean-regular stochastic processes

Published online by Cambridge University Press:  22 June 2010

SIEGFRIED GRAF
Affiliation:
Universität Passau, Fakultät für Informatik und Mathematik, D-94030 Passau, Germany. e-mail: [email protected]
HARALD LUSCHGY
Affiliation:
Universität Trier, FB IV-Mathematik, D-54286 Trier, Germany. e-mail: [email protected]
GILLES PAGÈS
Affiliation:
Laboratoire de Probabilités et Modèles aléatoires, UMR 7599, Université Paris 6, case 188, 4, pl. Jussieu, F-75252 Paris Cedex 5, France. e-mail: [email protected]

Abstract

We investigate the functional quantization problem for stochastic processes with respect to Lp(IRd, μ)-norms, where μ is a fractal measure namely, μ is self-similar or a homogeneous Cantor measure. The derived functional quantization upper rate bounds are universal depending only on the mean-regularity index of the process and the quantization dimension of μ and as universal rates they are optimal. Furthermore, for arbitrary Borel probability measures μ we establish a (nonconstructive) link between the quantization errors of μ and the functional quantization errors of the process in the space Lp(IRd, μ).

Type
Research Article
Copyright
Copyright © Cambridge Philosophical Society 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Creutzig, J. Approximation of Gaussian random vectors in Banach spaces. Ph.D. thesis (University of Jena, 2002).Google Scholar
[2]Delattre, S., Graf, S., Luschgy, H. and Pagès, G.Quantization of probability distributions under norm-based distortion measures. Statist. Decisions 22 (2004), 261282.CrossRefGoogle Scholar
[3]Dereich, S. High resolution coding of stochastic processes and small ball probabilities. Ph.D. thesis (TU Berlin, 2003).Google Scholar
[4]Dereich, S., Fehringer, F., Matoussi, A. and Scheutzow, M.On the link between small ball probabilities and the quantization problem for Gaussian measures on Banach spaces. J. Theoret. Probab. 16 (2003), 249265.CrossRefGoogle Scholar
[5]Falconer, K.Techniques in Fractal Geometry (Wiley, 1997).Google Scholar
[6]Graf, S.On Bandt's tangential distribution for self-similar measures. Monatsh. Math. 120 (1995), 223246.CrossRefGoogle Scholar
[7]Graf, S. and Luschgy, H.Foundations of Quantization for Probability Distributions (Springer, 2000).CrossRefGoogle Scholar
[8]Graf, S. and Luschgy, H.Asymptotics of the quantization errors for self-similar probabilities. Real Anal. Exchange 26 (2000) 01, 795810.CrossRefGoogle Scholar
[9]Graf, S. and Luschgy, H.The quantization dimension of self-similar probabilities. Math. Nachri. 241 (2002), 103109.3.0.CO;2-J>CrossRefGoogle Scholar
[10]Graf, S. and Luschgy, H.The point density measure in the quantization of self-similar probabilities. Math. Proc. Camb. Phil. Soc. 138 (2005), 513531.CrossRefGoogle Scholar
[11]Graf, S., Luschgy, H. and Pagès, G.Functional quantization and small ball probabilities for Gaussian processes. J. Theoret. Probab. 16 (2003), 10471062.CrossRefGoogle Scholar
[12]Graf, S., Luschgy, H. and Pagès, G.Optimal quantizers for Radon random vectors in a Banach space. J. Approx. Theory 144 (2007), 2753.CrossRefGoogle Scholar
[13]Gruber, P. M.Optimum quantization and its applications. Adv. Math. 186 (2004), 456497.CrossRefGoogle Scholar
[14]Hutchinson, J.Fractals and self-similarity. Indiana Univ. J. 30 (1981), 713747.CrossRefGoogle Scholar
[15]Kesseböhmer, M. and Zhu, S.Stability of quantization dimension and quantization for homogeneous Cantor measures. Math. Nachr. 280 (2007), 116.CrossRefGoogle Scholar
[16]Kreitmeier, W.Optimal quantization for dyadic homogeneous Cantor distributions. Math. Nachr. 281 (2008), 13071327.CrossRefGoogle Scholar
[17]Lifshits, M. A., Linde, W. and Shi, Z.Small deviations of Riemann–Liouville processes in Lq-spaces with respect to fractal measures. Proc. London Math. Soc. 92 (2006), 224250.CrossRefGoogle Scholar
[18]Lifshits, M. A., Linde, W. and Shi, Z.Small deviations of Gaussian random fields in Lq-spaces. Electron. J. Probab. 11 (2006), 12041233.CrossRefGoogle Scholar
[19]Lindsay, L. and Mauldin, R. D.Quantization dimension for conformal iterated function systems. Nonlinearity 15 (2002), 189199.CrossRefGoogle Scholar
[20]Luschgy, H. and Pagès, G.Sharp asymptotics of the functional quantization problem for Gaussian processes. Ann. Probab. 32 (2004), 15741599.CrossRefGoogle Scholar
[21]Luschgy, H. and Pagès, G.Functional quantization rate and mean pathwise regularity of processes with an application to Lévy processes. Ann. Appl. Probab. 18 (2008), 427469.CrossRefGoogle Scholar
[22]Luschgy, H. and Pagès, G.Moment estimates for Lévy processes. Electron. Comm. Probab. 13 (2008), 422434.CrossRefGoogle Scholar
[23]Nazarov, A. I.Logarithmic L 2-small ball asymptotics with respect to self-similar measures for some Gaussian random processes. J. Math. Sci. 133 (2006), 13141327.CrossRefGoogle Scholar
[24]Pötzelberger, K.The quantization error of self-similar distributions. Math. Proc. Camb. Phil. Soc. 137 (2004), 725740.CrossRefGoogle Scholar