Abstract
Stationary stochastic processes are analyzed at the level of their first and second order characteristics, mean and covariance, using Fourier methods.
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Notes
- 1.
See BCPT, Chap. VI.
- 2.
BCPT, p. 168.
- 3.
See BCPT, p.110.
- 4.
- 5.
Stationary (translation invariant) and self-similar phenomena are of rather widespread interest in the sciences, especially in connection with critical phenomena. Thus the stochastic models for such phenomena typically involve random measures and/or generalized functions.
- 6.
See BCPT, p.119.
- 7.
See BCPT, p. 112.
- 8.
See Bhattacharya and Waymire (2021) or BCPT, p.180.
- 9.
A metaphor for a stationary sequence is a process viewed as a data stream movie for which statistically it does not matter when one arrives at the theater. Time-reversibility permits it to also be run backward without changing the stochastic structure.
References
Bhattacharya R, Waymire E (2021) Random walk, Brownian motion, and martingales. Graduate text in mathematics. Springer, New York
Brockwell P, Davis R (1991) Time series: theory and methods. Springer series in statistics. Springer, New York
Grenander U (1981) Abstract inference. Wiley, New York
Lévy P (1953) Random functions: general theory with special reference to Laplacian random functions (Vol. 1, No. 12). University of California Press
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Bhattacharya, R., Waymire, E. (2022). Weakly Stationary Processes and Their Spectral Measures. In: Stationary Processes and Discrete Parameter Markov Processes. Graduate Texts in Mathematics, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-031-00943-3_2
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