Abstract:
Maximum likelihood (ML) is a popular and widely used statistical method, and while it is effective, its major short-comings are that it is a biased and non robust estimat...Show MoreMetadata
Abstract:
Maximum likelihood (ML) is a popular and widely used statistical method, and while it is effective, its major short-comings are that it is a biased and non robust estimator. This paper proposes a formal establishment of an optimisation of ML (OML) by approximating the true distribution minimising the bias, and exploiting the underlying relationship between ML and the maximum entropy method. OML exposes the inefficiency of the classical ML in the orthogonal least square error minimisation sense, for a number of finite sample datasets. The robustness of the proposed OML method in finding an estimate within the boundaries of the parameter space is also proven. Under the same conditions, OML consistently provides a more global and efficient estimation, so both theoretically and empirically establishing its superiority over ML in terms of efficiency and robustness
Published in: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings
Date of Conference: 14-19 May 2006
Date Added to IEEE Xplore: 24 July 2006
Print ISBN:1-4244-0469-X