A nonparametric estimator of the spectral density of a continuous-time Gaussian process observed at random times

TL;DR

This paper introduces a wavelet-based nonparametric estimator for the spectral density of Gaussian processes observed at random times, with proven asymptotic properties and applications to biological data.

Contribution

It develops a novel wavelet-based spectral density estimator for Gaussian processes observed at random times, extending existing methods to more general observation schemes.

Findings

Estimator satisfies a central limit theorem with convergence depending on process roughness

Achieves optimal convergence rate similar to periodogram-based estimators

Applicable to biological data and various random observation schemes

Abstract

In numerous applications data are observed at random times and an estimated graph of the spectral density may be relevant for characterizing and explaining phenomena. By using a wavelet analysis, one derives a nonparametric estimator of the spectral density of a Gaussian process with stationary increments (or a 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 moment of random durations between successive observations. 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.