Least L/sub p/-norm estimation of autoregressive model coefficients of symmetric /spl alpha/-stable processes

Abstract

Most of the existing coefficient estimation techniques in the literature for autoregressive (AR) symmetric /spl alpha/-stable (S/spl alpha/S) processes require large amounts of data for efficient estimation. However, in many practical cases, either only a short length of data is available or the data is nonstationary. Motivated by the norm of /spl alpha/-stable variables, the AR model coefficient estimation problem is formulated as an l/sub p/-norm minimization problem, and the interactively reweighted least squares (IRLS) is suggested for the solution. The simulation results indicate superior performance when compared to existing methods, especially when only short length data are available.