Filtering of the ARMAX Process with Generalized <i>t</i>-Distribution Noise: The Influence Function Approach

Abstract

The commonly made assumption of Gaussian noise is an approximation to reality. In this paper, influence function, an analysis tool in robust statistics, is used to formulate a recursive solution for the filtering of the ARMAX process with generalized t -distribution noise. By being a superset encompassing Gaussian, uniform, t, and double exponential distributions, generalized t -distribution has the flexibility of characterizing noise with Gaussian or non-Gaussian statistical properties. The filter is formulated as a maximum likelihood problem, but instead of solving the optimization problem numerically, influence function approximation is used to obtain a recursive solution to reduce the computational load and facilitate real-time implementation. The influence function equations derived are also useful in determining the variance of the filter and the impact of outliers.