On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model
This provides a robust method for signal processing and time series analysis in the presence of outliers or heavy-tailed noise, but it is incremental as it adapts LAD to a specific model.
The paper tackles parameter estimation for a 2-dimensional sinusoidal model by proposing robust least absolute deviation (LAD) estimators as an alternative to least squares, establishing their strong consistency and asymptotic normality, and demonstrating advantages through simulations and texture data analysis.
Estimation of the parameters of a 2-dimensional sinusoidal model is a fundamental problem in digital signal processing and time series analysis. In this paper, we propose a robust least absolute deviation (LAD) estimators for parameter estimation. The proposed methodology provides a robust alternative to non-robust estimation techniques like the least squares estimators, in situations where outliers are present in the data or in the presence of heavy tailed noise. We study important asymptotic properties of the LAD estimators and establish the strong consistency and asymptotic normality of the LAD estimators of the signal parameters of a 2-dimensional sinusoidal model. We further illustrate the advantage of using LAD estimators over least squares estimators through extensive simulation studies. Data analysis of a 2-dimensional texture data indicates practical applicability of the proposed LAD approach.