A BERRY-ESSEEN BOUND WITH APPLICATIONS TO VERTEX DEGREE COUNTS IN THE ERDOS-RENYI RANDOM GRAPH

• 成果类型: Article
• 署名作者: Goldstein, Larry
• 署名单位: University of Southern California
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/12-AAP848
• 发表日期: 2013
• 页码: 617-636
• 关键词: normal approximation; Poisson

摘要

Applying Stein's method, an inductive technique and size bias coupling yields a Berry-Esseen theorem for normal approximation without the usual restriction that the coupling be bounded. The theorem is applied to counting the number of vertices in the Erdos-Renyi random graph of a given degree.