A note on asymptotic distributions in directed exponential random graph models with bi-degree sequences
| Autor: | Jing Luo, Laala Zeyneb, Ting Yan, Hong Qin |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Statistics and Probability Asymptotic analysis Degree (graph theory) Dimension (graph theory) Estimator Asymptotic distribution Maximum likelihood sequence estimation 01 natural sciences Combinatorics 010104 statistics & probability 03 medical and health sciences 030104 developmental biology Exponential random graph models Applied mathematics Statistics::Methodology 0101 mathematics Mathematics Central limit theorem |
| DOI: | 10.6084/m9.figshare.3619746 |
| Popis: | The asymptotic normality of a fixed number of the maximum likelihood estimators in the directed exponential random graph models with an increasing bi-degree sequence has been established recently. In this paper, we further derive a central limit theorem for a linear combination of all the maximum likelihood estimators with an increasing dimension. Simulation studies are provided to illustrate the asymptotic results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|