On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model

• URL: http://arxiv.org/abs/2301.03229v2
• Date: Thu, 15 Jun 2023 20:46:53 GMT
• Title: On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model
• Authors: Saptarshi Roy, Amit Mitra and N K Archak
• Abstract summary: We propose a robust least absolute deviation (LAD) estimators for parameter estimation. We establish the strong consistency and normality of the LAD estimators of the signal parameters of a 2-dimensional sinusoidal model. Data analysis of a 2-dimensional texture data indicates practical applicability of the proposed LAD approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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