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Proceedings Paper

Statistical study of the inverse first passage time algorithm
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Paper Abstract

We discuss a method that analyzes time series generated by point processes to detect possible non stationarity in the data. We interpret each observation as the first passage time of a stochastic process through a deterministic boundary and we concentrate the effect of different dynamics on the boundary shape. We propose an estimator for the boundary and we compute its confidence intervals. Applying the Inverse First Passage Time Algorithm we then recognize the evolution in the dynamics of the time series by means of a comparison of the boundary shapes. This is performed using a suitable time window fragmentation on the observed data.

Paper Details

Date Published: 7 June 2007
PDF: 8 pages
Proc. SPIE 6603, Noise and Fluctuations in Photonics, Quantum Optics, and Communications, 66030N (7 June 2007); doi: 10.1117/12.725681
Show Author Affiliations
Laura Sacerdote, Univ. di Torino (Italy)
Cristina Zucca, Univ. di Torino (Italy)

Published in SPIE Proceedings Vol. 6603:
Noise and Fluctuations in Photonics, Quantum Optics, and Communications
Leon Cohen, Editor(s)

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