Likelihood-Based Sufficient Dimension Reduction

• 成果类型: Article
• 署名作者: Cook, R. Dennis; Forzani, Liliana
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); National University of the Littoral
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0106
• 发表日期: 2009
• 页码: 197-208
• 关键词: sliced inverse regression

摘要

We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality.