Robust Estimation for Spatial Autoregressive Processes Based on Bounded Innovation Propagation Representations

OJEDA, SILVIA MARÍA; BRITOS, GRISEL MARIBEL

COMPUTATIONAL STATISTICS (ZEITSCHRIFT)

SPRINGER HEIDELBERG

Año: 2018 p. 1 - 21

0943-4062

Paper Under ReviewRobust methods have been a successful approach to deal with contaminations and noises in image processing. In this paper, we introduce a new robust method for two-dimensional autoregressivemodels. Our method, called BMM-2D, relies on representing a two-dimensional autoregressive process with an auxiliary model to attenuate the effect of contamination (outliers). We compare the performanceof our method with existing robust estimators and the least squares estimator via a comprehensive Monte Carlo simulation study which considers different levels of replacement contamination and windowsizes. The results show that the new estimator is superior to the other estimators, both in accuracy and precision. An application to image filtering highlights the findings and illustrates how the estimatorworks in practical applications.