This document summarizes a dissertation that develops new methods for approximating the parameters of stochastic signal distributions from time series data using moment-cumulant based approaches. Specifically, it aims to improve the accuracy of parameter estimation for non-Gaussian signals that arise in applications like structural health monitoring and evaluating product quality. The topic is relevant, particularly for Ukraine, and builds on previous research at the author's university.
This document summarizes a dissertation that develops new methods for approximating the parameters of stochastic signal distributions from time series data using moment-cumulant based approaches. Specifically, it aims to improve the accuracy of parameter estimation for non-Gaussian signals that arise in applications like structural health monitoring and evaluating product quality. The topic is relevant, particularly for Ukraine, and builds on previous research at the author's university.