Theoretical and Practical Limits of Signal Strength Estimate Precision for Kolmogorov-Zurbenko Periodograms with Dynamic Smoothing

TL;DR

This paper analyzes the limits of signal strength estimate precision in Kolmogorov-Zurbenko periodograms with dynamic smoothing, comparing them to static smoothing methods, and proposes a two-step spectral analysis protocol.

Contribution

It introduces a theoretical framework for confidence intervals of signal strength estimates and suggests a two-step approach combining dynamic and static smoothing for improved spectral analysis.

Findings

Dynamic smoothing provides broader confidence intervals for signal strength.

Static smoothing can yield more precise signal strength estimates.

A two-step protocol enhances spectral analysis accuracy.

Abstract

This investigation establishes the theoretical and practical limits of signal strength estimate precision for Kolmogorov-Zurbenko periodograms with dynamic smoothing and compares them to those of standard log-periodograms with static smoothing. Previous research has established the sensitivity, accuracy, resolution, and robustness of Kolmogorov-Zurbenko periodograms with dynamic smoothing in estimating signal frequencies. However, the precision with which they estimate signal strength has never been evaluated. To this point, the width of the confidence interval for a signal strength estimate can serve as a criterion for assessing the precision of such estimates: the narrower the confidence interval, the more precise the estimate. The statistical background for confidence intervals of periodograms is presented, followed by candidate functions to compute and plot them when using Kolmogorov-Zurbenko periodograms with dynamic smoothing. Given an identified signal frequency, a static smoothing window and its smoothing window width can be selected such that its confidence interval is narrower and, thus, its signal strength estimate more precise, than that of dynamic smoothing windows, all while maintaining a level of frequency resolution as good as or better than that of a dynamic smoothing window. These findings suggest the need for a two-step protocol in spectral analysis: computation of a Kolmogorov-Zurbenko periodogram with dynamic smoothing to detect, identify, and separate signal frequencies, followed by computation of a Kolmogorov-Zurbenko periodogram with static smoothing to precisely estimate signal strength and compute its confidence intervals.