Asymptotically optimal quantization schemes for Gaussian processes

Abstract

We describe quantization designs which lead to asymptotically and order optimal functional quantizers. Regular variation of the eigenvalues of the covariance operator plays a crucial role to achieve these rates. For the development of a constructive quantization scheme we rely on the knowledge of the eigenvectors of the covariance operator in order to transform the problem into a finite dimensional quantization problem of normal distributions. Furthermore we derive a high-resolution formula for the $L^2$-quantization errors of Riemann-Liouville processes.

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References in corpus (1)

• Sharp asymptotics of the functional quantization problem for Gaussian processes

Cited by in corpus (2)

• Convergence of multi-dimensional quantized $SDE$'s
• Quadratic optimal functional quantization of stochastic processes and numerical applications