Department of Statistics

Permanent URI for this collectionhttp://197.255.125.131:4000/handle/123456789/23133

Browse

Search Results

Now showing 1 - 3 of 3
  • Item
    Large deviation result for the empirical locality measure of typed random geometric graphs
    (International journal of Statistics and Probability, 2015) Doku-Amponsah, K
    In this article for a finite typed random geometric graph we define the empirical locality distribution, which records the number of nodes of a given type linked to a given number of nodes of each type. We find large deviation principle (LDP) for the empirical locality measure given the empirical pair measure and the empirical type measure of the typed random geometric graphs. From this LDP, we derive large deviation principles for the degree measure and the proportion of detached nodes in the classical Erd˝os-R´enyi graph defined on [0, 1]d. This graphs have been suggested by (Canning and Penman, 2003) as a possible extension to the randomly typed random graphs.
  • Item
    Large deviation principle for the empirical degree measure of preferential attachment random graphs.
    (International journal of Statistics and Probability, 2015) Doku-Amponsah, K; Mettle, F.O; Nortey, E.N.N
    We consider preferential attachment random graphs which may be obtained as follows: It starts with a single node. If a new node appears, it is linked by an edge to one or more existing node(s) with a probability proportional to function of their degree. For a class of linear preferential attachment random graphs we find a large deviation principle (LDP) for the empirical degree measure. In the course of the prove this LDP we establish an LDP for the empirical degree and pair distribution see Theorem 2.3, of the fitness preferential attachment model of random graphs.
  • Item
    Exponential approximation,method of types for empirical neighbourhood distributions for random graphs by random allocation
    (International journal of Statistics and Probability, 2014) Doku-Amponsah, K
    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.