This work generalizes the theoretical form of Allan variance to include nonstationary processes, enabling more accurate interpretation and noise pattern detection in both stationary and nonstationary cases
This paper enhances the Generalized Method of Wavelet Moments (GMWM) for parametric estimation of stochastic error signals by incorporating model moments, improving its statistical efficiency and finite sample performance to rival maximum likelihood estimation, with demonstrated benefits in inertial sensor calibration
This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: An R Package for Robust Time Series Analysis.
This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Fast and Robust Parametric Estimation for Time Series and Spatial Models.
This publication contributes to work in model selection, signal processing, with a focus reflected in its title: On the Identifiability of Latent Models for Dependent Data.
This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: The gmwm R Package: A Comprehensive Tool for Time Series Analysis From State-Space Models to Robustness.
This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Wavelet Variance for Random Fields: An M-Estimation Framework.
This work formally proves the statistical inconsistency of Allan variance-based parameter estimation for latent models, highlighting its limitations, especially in inertial sensor calibration, and contrasting it with a statistically sound alternative