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Realizations and Factorizations of Positive Definite Kernels

Author

Listed:
  • Palle Jorgensen

    (The University of Iowa)

  • Feng Tian

    (Hampton University)

Abstract

Given a fixed sigma-finite measure space $$\left( X,\mathscr {B},\nu \right) $$ X , B , ν , we shall study an associated family of positive definite kernels K. Their factorizations will be studied with view to their role as covariance kernels of a variety of stochastic processes. In the interesting cases, the given measure $$\nu $$ ν is infinite, but sigma-finite. We introduce such positive definite kernels $$K\left( \cdot ,\cdot \right) $$ K · , · with the two variables from the subclass of the sigma-algebra $$\mathscr {B}$$ B whose elements are sets with finite $$\nu $$ ν measure. Our setting and results are motivated by applications. The latter are covered in the second half of the paper. We first make precise the notions of realizations and factorizations for K, and we give necessary and sufficient conditions for K to have realizations and factorizations in $$L^{2}\left( \nu \right) $$ L 2 ν . Tools in the proofs rely on probability theory and on spectral theory for unbounded operators in Hilbert space. Applications discussed here include the study of reversible Markov processes, and realizations of Gaussian fields, and their Ito-integrals.

Suggested Citation

  • Palle Jorgensen & Feng Tian, 2019. "Realizations and Factorizations of Positive Definite Kernels," Journal of Theoretical Probability, Springer, vol. 32(4), pages 1925-1942, December.
  • Handle: RePEc:spr:jotpro:v:32:y:2019:i:4:d:10.1007_s10959-018-0868-3
    DOI: 10.1007/s10959-018-0868-3
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    References listed on IDEAS

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    1. Masayoshi Takeda, 2011. "A Large Deviation Principle for Symmetric Markov Processes with Feynman–Kac Functional," Journal of Theoretical Probability, Springer, vol. 24(4), pages 1097-1129, December.
    2. Dmitry Korshunov, 2008. "The Key Renewal Theorem for a Transient Markov Chain," Journal of Theoretical Probability, Springer, vol. 21(1), pages 234-245, March.
    3. El Machkouri, Mohamed & Es-Sebaiy, Khalifa & Ouassou, Idir, 2017. "On local linear regression for strongly mixing random fields," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 103-115.
    4. Antoine Ayache & Werner Linde, 2008. "Approximation of Gaussian Random Fields: General Results and Optimal Wavelet Representation of the Lévy Fractional Motion," Journal of Theoretical Probability, Springer, vol. 21(1), pages 69-96, March.
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