Institute of Information Theory and Automation

Publication details

Parameter estimation of sub-Gaussian stable distributions

Journal Article

Omelchenko Vadym

serial: Kybernetika vol.50, 6 (2014), p. 929-949

project(s): GA13-14445S, GA ČR

keywords: stable distribution, sub-Gaussian distribution, maximum likelihood

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abstract (eng):

In this paper, we present a parameter estimation method for sub-Gaussian stable distributions. Our algorithm has two phases: in the first phase, we calculate the average values of harmonic functions of observations and in the second phase, we conduct the main procedure of asymptotic maximum likelihood where those average values are used as inputs. This implies that the main procedure of our method does not depend on the sample size of observations. The main idea of our method lies in representing the partial derivative of the density function with respect to the parameter that we estimate as the sum of harmonic functions and using this representation for finding this parameter. For fifteen summands we get acceptable precision. We demonstrate this methodology on estimating the tail index and the dispersion matrix of sub-Gaussian distributions.


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Institute of Information Theory and Automation