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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F14%3A10284117%21RIV15-MSM-11320___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
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 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. 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 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.
dcterms:title
Approximated maximum likelihood estimation of parameters of discrete stable family Approximated maximum likelihood estimation of parameters of discrete stable family
skos:prefLabel
Approximated maximum likelihood estimation of parameters of discrete stable family Approximated maximum likelihood estimation of parameters of discrete stable family
skos:notation
RIV/00216208:11320/14:10284117!RIV15-MSM-11320___
n3:aktivita
n16:P n16:I
n3:aktivity
I, P(GAP203/12/0665)
n3:cisloPeriodika
6
n3:dodaniDat
n7:2015
n3:domaciTvurceVysledku
n4:1682458 n4:3348725
n3:druhVysledku
n17:J
n3:duvernostUdaju
n11:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
3927
n3:idVysledku
RIV/00216208:11320/14:10284117
n3:jazykVysledku
n18:eng
n3:klicovaSlova
maximum likelihood; parameter estimation; discrete stable distribution
n3:klicoveSlovo
n8:maximum%20likelihood n8:parameter%20estimation n8:discrete%20stable%20distribution
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[26296AB91AC2]
n3:nazevZdroje
Kybernetika
n3:obor
n13:BA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n15:GAP203%2F12%2F0665
n3:rokUplatneniVysledku
n7:2014
n3:svazekPeriodika
50
n3:tvurceVysledku
Klebanov, Lev Slámová, Lenka
s:issn
0023-5954
s:numberOfPages
12
n10:organizacniJednotka
11320