In this note,the authors revisit the envelope dimension reduction,which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance.Motivated by the recent dev...In this note,the authors revisit the envelope dimension reduction,which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance.Motivated by the recent developments in envelope methods and algorithms,the authors refresh the envelope inverse regression as a flexible alternative to the existing inverse regression methods in dimension reduction.The authors discuss the versatility of the envelope approach and demonstrate the advantages of the envelope dimension reduction through simulation studies.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.12301365supported by the National Natural Science Foundation of China under Grant No.2241200071Guangdong Basic and Applied Basic Research Foundation under Grant No.2023A1515110001。
文摘In this note,the authors revisit the envelope dimension reduction,which was first introduced for estimating a sufficient dimension reduction subspace without inverting the sample covariance.Motivated by the recent developments in envelope methods and algorithms,the authors refresh the envelope inverse regression as a flexible alternative to the existing inverse regression methods in dimension reduction.The authors discuss the versatility of the envelope approach and demonstrate the advantages of the envelope dimension reduction through simulation studies.
