ASYMPTOTIC NORMALITY OF MAXIMUM LIKELIHOOD AND ITS VARIATIONAL APPROXIMATION FOR STOCHASTIC BLOCKMODELS

• 成果类型: Article
• 署名作者: Bickel, Peter; Choi, David; Chang, Xiangyu; Zhang, Hai
• 署名单位: University of California System; University of California Berkeley; Carnegie Mellon University; Xi'an Jiaotong University; Northwest University Xi'an
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/13-AOS1124
• 发表日期: 2013
• 页码: 1922-1943
• 关键词: graphs; MODEL

摘要

Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that applies such methods to network data, and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data, by either maximum likelihood or variational estimation. The result also applies to various sub-models of the stochastic blockmodel found in the literature.