Geodesic
Approximated maximum likelihood estimation of parameters of discrete stable family
Kybernetika, Tome 50 (2014) no. 6, pp. 1065-1076

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In this article we propose a method of parameters estimation for the class of discrete stable laws. Discrete stable distributions form a discrete analogy to classical stable distributions and share many interesting properties with them such as heavy tails and skewness. Similarly as stable laws discrete stable distributions are defined through characteristic function and do not posses a probability mass function in closed form. This inhibits the use of classical estimation methods such as maximum likelihood and other approach has to be applied. We depart from the $\mathcal{H}$-method of maximum likelihood suggested by Kagan (1976) where the likelihood function is replaced by a function called informant which is an approximation of the likelihood function in some Hilbert space. For this method only some functionals of the distribution are required, such as probability generating function or characteristic function. We adopt this method for the case of discrete stable distributions and in a simulation study show the performance of this method.
DOI : 10.14736/kyb-2014-6-1065
Classification : 60E07, 60E10, 62F12, 62G05, 65C60
Keywords: discrete stable distribution; parameter estimation; maximum likelihood 
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     title = {Approximated maximum likelihood estimation of parameters of discrete stable family},
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     pages = {1065--1076},
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Slámová, Lenka; Klebanov, Lev B. Approximated maximum likelihood estimation of parameters of discrete stable family. Kybernetika, Tome 50 (2014) no. 6, pp. 1065-1076. doi: 10.14736/kyb-2014-6-1065

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