Modified likelihood root in high dimensions

• 成果类型: Article
• 署名作者: Tang, Yanbo; Reid, Nancy
• 署名单位: University of Toronto; Vector Institute for Artificial Intelligence
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12389
• 发表日期: 2020
• 页码: 1349-1369
• 关键词: ASYMPTOTIC-BEHAVIOR; profile; parameters; inference; models

摘要

We examine a higher order approximation to the significance function with increasing numbers of nuisance parameters, based on the normal approximation to an adjusted log-likelihood root. We show that the rate of the correction for nuisance parameters is larger than the correction for non-normality, when the parameter dimensionpisO(n(alpha)) for alpha<12. We specialize the results to linear exponential families and location-scale families and illustrate these with simulations.