عنوان

Central limit theorems and statistical inference for some random graph models

Number

TLets810055

Title Proper

Central limit theorems and statistical inference for some random graph models

General Material Designation

[Thesis]

First Statement of Responsibility

Baaqeel, Hanan

Name of Publisher, Distributor, etc.

University of Nottingham

Date of Publication, Distribution, etc.

2015

Dissertation or thesis details and type of degree

Thesis (Ph.D.)

Text preceding or following the note

2015

Text of Note

Random graphs and networks are of great importance in any fields including mathematics, computer science, statistics, biology and sociology. This research aims to develop statistical theory and methods of statistical inference for random graphs in novel directions. A major strand of the research is the development of conditional goodness-of-fit tests for random graph models and for random block graph models. On the theoretical side, this entails proving a new conditional central limit theorem for a certain graph statistics, which are closely related to the number of two-stars and the number of triangles, and where the conditioning is on the number of edges in the graph. A second strand of the research is to develop composite likelihood methods for estimation of the parameters in exponential random graph models. Composite likelihood methods based on edge data have previously been widely used. A novel contribution of the thesis is the development of composite likelihood methods based on more complicated data structures. The goals of this PhD thesis also include testing the numerical performance of the novel methods in extensive simulation studies and through applications to real graphical data sets.

QA273 Probabilities

University of Nottingham

University of Nottingham

Electronic name

p

[Thesis]

276903

a

Y