Stawinoga, Agnieszka (2010) ASSESSMENT OF STOCHASTIC APPROXIMATION METHODS AND OF DEGENERACY DIAGNOSTIC TOOLS IN EXPONENTIAL RANDOM GRAPH MODELS. [Tesi di dottorato] (Unpublished)
Preview |
PDF
stawinoga_agnieszka_23.pdf Download (3MB) | Preview |
| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | ASSESSMENT OF STOCHASTIC APPROXIMATION METHODS AND OF DEGENERACY DIAGNOSTIC TOOLS IN EXPONENTIAL RANDOM GRAPH MODELS |
| Creators: | Creators Email Stawinoga, Agnieszka agnieszka.stawinoga@unina.it |
| Date: | 30 November 2010 |
| Number of Pages: | 148 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Matematica e statistica |
| Scuola di dottorato: | Scienze economiche e statistiche |
| Dottorato: | Statistica |
| Ciclo di dottorato: | 23 |
| Coordinatore del Corso di dottorato: | nome email Lauro, Carlo Natale clauro@unina.it |
| Tutor: | nome email Giordano, Giuseppe ggiordan@unisa.it |
| Date: | 30 November 2010 |
| Number of Pages: | 148 |
| Keywords: | statistical models for social networks, Exponential Random Graph Models, degeneracy, stochastic approximation methods |
| Settori scientifico-disciplinari del MIUR: | Area 13 - Scienze economiche e statistiche > SECS-S/01 - Statistica |
| Date Deposited: | 09 Dec 2010 21:46 |
| Last Modified: | 05 Dec 2014 14:36 |
| URI: | http://www.fedoa.unina.it/id/eprint/8357 |
| DOI: | 10.6092/UNINA/FEDOA/8357 |
Collection description
In recent decades there has been an enormous growth of interest in the notion of social network and the methods of Social Network Analysis (SNA). The methodology developed in the field of network analysis has been categorized into descriptive methods and statistical methods. The statistical methods may be organized into two parts; the first group consists of dyadic and triadic methods which represent statistical models for subgraphs and the second group of statistical models for entire graphs and digraphs. In this work we pay attention to the Exponential Random Graph Models (ERGMs), the statistical models which provide a general framework for modeling dependent data where the dependence can be thought of as a neighborhood effect. The present manuscript is based on two main motivations. Firstly, we are interested to examine model diagnostics and check for degeneracy of ERGMs using different methods and functions. Secondly, we aim to evaluate and compare results obtained for networks of various sizes from three different estimation procedures such as Newton-Raphson, Robbins-Monro and Stepping.
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |
