Consistent estimation of non-bandlimited spectral density from uniformly spaced samples

TL;DR

This paper demonstrates that, with increasing sampling rate, the smoothed periodogram from uniform samples consistently estimates the spectral density of a stationary process, even if it is not bandlimited, challenging the preference for irregular sampling.

Contribution

It shows that regular sampling can yield consistent spectral density estimates with high sampling rates, providing a theoretical basis and practical guidelines for sampling choices.

Findings

Consistent estimation achieved with high-rate uniform sampling.

Uniform sampling has less variance and bias than Poisson sampling.

Theoretical and simulation results support the approach.

Abstract

In the matter of selection of sample time points for the estimation of the power spectral density of a continuous time stationary stochastic process, irregular sampling schemes such as Poisson sampling are often preferred over regular (uniform) sampling. A major reason for this preference is the well-known problem of inconsistency of estimators based on regular sampling, when the underlying power spectral density is not bandlimited. It is argued in this paper that, in consideration of a large sample property like consistency, it is natural to allow the sampling rate to go to infinity as the sample size goes to infinity. Through appropriate asymptotic calculations under this scenario, it is shown that the smoothed periodogram based on regularly spaced data is a consistent estimator of the spectral density, even when the latter is not band-limited. It transpires that, under similar assumptions, the estimators based on uniformly sampled and Poisson-sampled data have about the same rate of convergence. Apart from providing this reassuring message, the paper also gives a guideline for practitioners regarding appropriate choice of the sampling rate. Theoretical calculations for large samples and Monte-Carlo simulations for small samples indicate that the smoothed periodogram based on uniformly sampled data have less variance and more bias than its counterpart based on Poisson sampled data.