메뉴 건너뛰기




Volumn 94, Issue 3, 2007, Pages 569-584

Dimension reduction in regression without matrix inversion

Author keywords

envelope; Central subspace; Singularity of sample covariance

Indexed keywords


EID: 34548529252     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asm038     Document Type: Article
Times cited : (82)

References (27)
  • 1
    • 0034178660 scopus 로고    scopus 로고
    • Corrections to test statistics in principal hessian directions
    • BENTLER, P. M. & XIE, J. (2000). Corrections to test statistics in principal hessian directions. Statist. Prob. Lett. 47, 381-9.
    • (2000) Statist. Prob. Lett , vol.47 , pp. 381-389
    • BENTLER, P.M.1    XIE, J.2
  • 3
    • 0036128489 scopus 로고    scopus 로고
    • Dimension reduction strategies for analyzing global gene expression data with a response
    • CHIAROMONTE, F. & MARTINELLI, J. A. (2002). Dimension reduction strategies for analyzing global gene expression data with a response. Math. Biosci. 176, 123-44.
    • (2002) Math. Biosci , vol.176 , pp. 123-144
    • CHIAROMONTE, F.1    MARTINELLI, J.A.2
  • 5
    • 0036284461 scopus 로고    scopus 로고
    • Dimension reduction for the conditional mean
    • COOK, R. D. & LI, B. (2002). Dimension reduction for the conditional mean. Ann. Statist. 30, 455-74.
    • (2002) Ann. Statist , vol.30 , pp. 455-474
    • COOK, R.D.1    LI, B.2
  • 6
    • 21644461782 scopus 로고    scopus 로고
    • Determining the dimension of Iterative Hessian Transformation
    • COOK, R. D. & LI, B. (2004). Determining the dimension of Iterative Hessian Transformation. Ann. Statist. 32, 2501-31.
    • (2004) Ann. Statist , vol.32 , pp. 2501-2531
    • COOK, R.D.1    LI, B.2
  • 7
    • 20444454672 scopus 로고    scopus 로고
    • Sufficient dimension reduction via inverse regression: A minimum discrepancy approach
    • COOK, R. D. & NI, L. (2005a). Sufficient dimension reduction via inverse regression: A minimum discrepancy approach. J. Am. Statist. Assoc. 100, 410-28.
    • (2005) J. Am. Statist. Assoc , vol.100 , pp. 410-428
    • COOK, R.D.1    NI, L.2
  • 8
    • 33644973603 scopus 로고    scopus 로고
    • Using intra-slice covariances for improved estimation of the central subspace in regression
    • COOK, R. D. & NI, L. (2005b). Using intra-slice covariances for improved estimation of the central subspace in regression. Biometrika 93, 65-74.
    • (2005) Biometrika , vol.93 , pp. 65-74
    • COOK, R.D.1    NI, L.2
  • 9
    • 34249753618 scopus 로고
    • Support vector network
    • CORTES, C. & VAPNIK, V. (1995). Support vector network. Mach. Learn. 20, 273-97.
    • (1995) Mach. Learn , vol.20 , pp. 273-297
    • CORTES, C.1    VAPNIK, V.2
  • 11
    • 0001493668 scopus 로고
    • Asymptotics of graphical projection pursuit
    • DIACONIS, P. & FREEDMAN, D. (1984). Asymptotics of graphical projection pursuit. Am. Statist. 12, 793-815.
    • (1984) Am. Statist , vol.12 , pp. 793-815
    • DIACONIS, P.1    FREEDMAN, D.2
  • 12
    • 0000975683 scopus 로고
    • Slicing regression: A link-free regression method
    • DUAN, N. & LI, K. C. (1991). Slicing regression: a link-free regression method. Ann. Statist. 19, 505-30.
    • (1991) Ann. Statist , vol.19 , pp. 505-530
    • DUAN, N.1    LI, K.C.2
  • 15
    • 0001735517 scopus 로고
    • On the mathematical foundations of theoretical statistics
    • FISHER, R. A. (1922). On the mathematical foundations of theoretical statistics. Phil. Trans. R Soc. Lond. A 222, 309-68.
    • (1922) Phil. Trans. R Soc. Lond. A , vol.222 , pp. 309-368
    • FISHER, R.A.1
  • 16
    • 0041753016 scopus 로고
    • On almost linearity of low dimensional projections from high dimensional data
    • HALL, P. & LI, K. C. (1993). On almost linearity of low dimensional projections from high dimensional data. Ann. Statist. 21, 867-89.
    • (1993) Ann. Statist , vol.21 , pp. 867-889
    • HALL, P.1    LI, K.C.2
  • 17
    • 33745886270 scopus 로고    scopus 로고
    • Classifier technology and the illusion of progress (with Discussion)
    • HAND, D. (2006). Classifier technology and the illusion of progress (with Discussion). Statist. Sci. 21, 1-34.
    • (2006) Statist. Sci , vol.21 , pp. 1-34
    • HAND, D.1
  • 18
    • 0000656711 scopus 로고
    • On the structure of partial least squares regression
    • HELLAND, I. S. (1990). On the structure of partial least squares regression. Scand. J. Statist. 17, 97-114.
    • (1990) Scand. J. Statist , vol.17 , pp. 97-114
    • HELLAND, I.S.1
  • 19
    • 0000263797 scopus 로고
    • Projection pursuit (with Discussion)
    • HUBER, P. (1985). Projection pursuit (with Discussion). Ann. Statist. 13, 435-525.
    • (1985) Ann. Statist , vol.13 , pp. 435-525
    • HUBER, P.1
  • 21
    • 0036392431 scopus 로고    scopus 로고
    • Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
    • LEDOIT, O. & WOLF, M. (2002). Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size. Ann. Statist. 30, 1081-102.
    • (2002) Ann. Statist , vol.30 , pp. 1081-1102
    • LEDOIT, O.1    WOLF, M.2
  • 22
    • 84945116550 scopus 로고
    • Sliced inverse regression for dimension reduction (with Discussion)
    • LI, K. C. (1991). Sliced inverse regression for dimension reduction (with Discussion). J. Am. Statist. Assoc. 86, 316-42.
    • (1991) J. Am. Statist. Assoc , vol.86 , pp. 316-342
    • LI, K.C.1
  • 23
    • 0001017987 scopus 로고
    • Regression analysis under link violation
    • LI, K. C. & DIJAN, N. (1989). Regression analysis under link violation. Ann. Statist. 17, 1009-52.
    • (1989) Ann. Statist , vol.17 , pp. 1009-1052
    • LI, K.C.1    DIJAN, N.2
  • 24
    • 12344271032 scopus 로고    scopus 로고
    • Dimension reduction methods for microarrays with application to censored survival data
    • LI, L. & LI, H. (2004). Dimension reduction methods for microarrays with application to censored survival data. Bioinformatics 20, 3406-12.
    • (2004) Bioinformatics , vol.20 , pp. 3406-3412
    • LI, L.1    LI, H.2
  • 26
    • 33845303175 scopus 로고    scopus 로고
    • ESPERR: Learning strong and weak signals in genomic sequence alignments to identify functional elements
    • TAYLOR, J., TYEKUCHEVA, S., KING, D. C., HARDISON, R. C., MILLER, W. & CHIAROMONTE, F. (2006). ESPERR: Learning strong and weak signals in genomic sequence alignments to identify functional elements. Genome Res. 16, 1596-604.
    • (2006) Genome Res , vol.16 , pp. 1596-1604
    • TAYLOR, J.1    TYEKUCHEVA, S.2    KING, D.C.3    HARDISON, R.C.4    MILLER, W.5    CHIAROMONTE, F.6
  • 27
    • 0036428498 scopus 로고    scopus 로고
    • An adaptive estimation of dimension reduction space (with Discussion)
    • XIA, Y., TONG, H., LI, W. K. & ZHU, L.-X. (2002). An adaptive estimation of dimension reduction space (with Discussion). J. R Statist. Soc. B 64, 363-410.
    • (2002) J. R Statist. Soc. B , vol.64 , pp. 363-410
    • XIA, Y.1    TONG, H.2    LI, W.K.3    ZHU, L.-X.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.