Optimal and sub-optimal quadratic forms for non-centered Gaussian processes

TL;DR

This paper develops a method to find the optimal quadratic forms for analyzing non-centered Gaussian processes, minimizing statistical uncertainty in measurements like MSD and VACF, with explicit formulas and practical comparisons.

Contribution

It introduces a spectral approach to construct optimal quadratic forms for non-centered Gaussian processes and provides explicit formulas for minimal cumulant moments.

Findings

Optimal quadratic form minimizes variance of measurements.

Explicit formula for the minimal achievable cumulant moment.

Comparison shows the optimal form outperforms traditional methods.

Abstract

Individual random trajectories of stochastic processes are often analyzed by using quadratic forms such as time averaged (TA) mean square displacement (MSD) or velocity auto-correlation function (VACF). The appropriate quadratic form is expected to have a narrow probability distribution in order to reduce statistical uncertainty of a single measurement. We consider the problem of finding the optimal quadratic form that minimizes a chosen cumulant moment (e.g., the variance) of the probability distribution, under the constraint of fixed mean value. For discrete non-centered Gaussian processes, we construct the optimal quadratic form by using the spectral representation of the cumulant moments. Moreover, we obtain a simple explicit formula for the smallest achievable cumulant moment that may serve as a quality benchmark for other quadratic forms. We illustrate the optimality issues by comparing the optimal variance with the variances of the TA MSD and TA VACF of fractional Brownian motion superimposed with a constant drift and independent Gaussian noise.