Sufficient dimension reduction in regressions across heterogeneous subpopulations

• 成果类型: Article
• 署名作者: Ni, LQ; Cook, RD
• 署名单位: State University System of Florida; University of Central Florida; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2005.00534.x
• 发表日期: 2006
• 页码: 89-107
• 关键词: sliced inverse regression; squares

摘要

Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-workers extended this method to regressions with qualitative predictors and developed a method, partial sliced inverse regression, under the assumption that the covariance matrices of the continuous predictors are constant across the levels of the qualitative predictor. We extend partial sliced inverse regression by removing the restrictive homogeneous covariance condition. This extension, which significantly expands the applicability of the previous methodology, is based on a new estimation method that makes use of a non-linear least squares objective function.