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Integration of covariance kernels and stationarity

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  • Lasinger, Rudolf

Abstract

The necessary and sufficient matrix condition of Mitchell, Morris and Ylvisaker (1990) for a stationary Gaussian process to have a specified process as kth derivative is investigated. The mean-square smoothing approach of stationary processes requires integration of covariance functions preserving stationarity. By providing a recursive representation of the involved reproducing kernel Hilbert spaces it is possible to analyse another criterion for k-fold integration of a process. This criterion only contains inequalities for the variances of the integrated processes. If the Hilbert space associated with the covariance function has a special form, which often occurs, then it can be shown that such processes can be integrated arbitrarily often. This is especially the case for the Ornstein-Uhlenbeck process. The results are applied to the linear and the exponential kernel and yield explicit norms in the corresponding reproducing kernel Hilbert spaces for each integration.

Suggested Citation

  • Lasinger, Rudolf, 1993. "Integration of covariance kernels and stationarity," Stochastic Processes and their Applications, Elsevier, vol. 45(2), pages 309-318, April.
  • Handle: RePEc:eee:spapps:v:45:y:1993:i:2:p:309-318
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    Cited by:

    1. Blanke, Delphine & Vial, CĂ©line, 2008. "Assessing the number of mean square derivatives of a Gaussian process," Stochastic Processes and their Applications, Elsevier, vol. 118(10), pages 1852-1869, October.

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