Exponential approximation,method of types for empirical neighbourhood distributions for random graphs by random allocation

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dc.contributor.author Doku-Amponsah, K
dc.date.accessioned 2015-06-23T11:59:50Z
dc.date.accessioned 2017-10-14T12:21:04Z
dc.date.available 2015-06-23T11:59:50Z
dc.date.available 2017-10-14T12:21:04Z
dc.date.issued 2014
dc.identifier.uri http://197.255.68.203/handle/123456789/6249
dc.description.abstract In this article we find exponential good approximation of the empirical neigbourhood distribution ofsymbolled random graphs conditioned to a given empirical symbol distribution and empirical pair distribution.Using this approximation we shorten or simplify the proof of (Doku-Amponsah and Morters 2010, Theorem 2.5);the large deviation principle (LDP) for empirical neigbourhood distribution of symbolled random graphs. We alsoshow that the LDP for the empirical degree measure of the classical Erd˝os-R´enyi graph is a special case of (Doku-Amponsah and Moerters, 2010, Theorem 2.5). From the LDP for the empirical degree measure, we derive an LDP for the the proportion of isolated vertices in the classical Erd˝os-R´enyi graph. en_US
dc.publisher International journal of Statistics and Probability en_US
dc.subject concentration inequalities en_US
dc.subject coupling en_US
dc.subject empirical occupancy measure en_US
dc.subject empirical degree measure en_US
dc.subject sparse random graphs en_US
dc.subject bins and balls en_US
dc.title Exponential approximation,method of types for empirical neighbourhood distributions for random graphs by random allocation en_US
dc.type Article en_US


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