A nonparametric estimation of the spectral density of a continuous-time Gaussian Process observed at random times
Résumé
In numerous applications (Biology, Finance, Internet Traffic, Oceanography,...) data are observed at random times and a graph of an estimation of the spectral density may be relevant for characterizing phenomena and explaining. By using a wavelet analysis, one derives a nonparametric estimator of the spectral density of a Gaussian process with stationary increments (also stationary Gaussian process) from the observation of one path at random discrete times. For every positive frequency, this estimator is proved to satisfy a central limit theorem with a convergence rate depending on the roughness of the process and the order moment of duration between times of observation. In the case of stationary Gaussian processes, one can compare this estimator with estimators based on the empirical periodogram. Both estimators reach the same optimal rate of convergence, but the estimator based on wavelet analysis converges for a different class of random times. Simulation examples and application to biological data are also provided.
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