GMM Estimation of 2D-RCA Models With Applications to Texture Image Classification

The 2D indexed random coefficients autoregressive (2D-RCA) models are obtained by introducing appropriate random field coefficients to an AR model on Z
²
. The study of such models is motivated by their capability to capture the space-varying behavior of the volatility. A generalized method of moment approach is considered to estimate the 2D-RCA models. Consistency and asymptotic normality of the estimates are derived. Estimated parameters are used, at a later stage, as pixel features in texture image classification.