Effects of data dimension on empirical likelihood

• 成果类型: Article
• 署名作者: Chen, Song Xi; Peng, Liang; Qin, Ying-Li
• 署名单位: Iowa State University; University System of Georgia; Georgia Institute of Technology
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp037
• 发表日期: 2009
• 页码: 711722
• 关键词: confidence-intervals; tests

摘要

We evaluate the effects of data dimension on the asymptotic normality of the empirical likelihood ratio for high-dimensional data under a general multivariate model. Data dimension and dependence among components of the multivariate random vector affect the empirical likelihood directly through the trace and the eigenvalues of the covariance matrix. The growth rates to infinity we obtain for the data dimension improve the rates of Hjort et al. (2008).