Maximum likelihood parameter estimation of the asymmetric generalised Gaussian family of distributions

It is useful to model and detect observed data with symmetric, asymmetric or long tail unimodal distributions. The algorithm based on the maximum likelihood function is developed for asymmetric generalised Gaussian family of distributions. The Cramer-Rao lower bound for this estimator is also investigated. The simulation results show that the proposed algorithm is robust and asymptotically unbiased