Marginal tests with sliced average variance estimation

• 成果类型: Article
• 署名作者: Shao, Yongwu; Cook, R. Dennis; Weisberg, Sanford
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asm021
• 发表日期: 2007
• 页码: 285296
• 关键词: sufficient dimension reduction; inverse regression; binary response

摘要

We present a new computationally feasible test for the dimension of the central subspace in a regression problem based on sliced average variance estimation. We also provide a marginal coordinate test. Under the null hypothesis, both the test of dimension and the marginal coordinate test involve test statistics that asymptotically have chi-squared distributions given normally distributed predictors, and have a distribution that is a linear combination of chi-squared distributions in general.