Central limit theorems and statistical inference for some random graph models
نام عام مواد
[Thesis]
نام نخستين پديدآور
Baaqeel, Hanan
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
University of Nottingham
تاریخ نشرو بخش و غیره
2015
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Thesis (Ph.D.)
امتياز متن
2015
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
QA273 Probabilities
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )