A minimum discrepancy approach to multivariate dimension reduction via k-means inverse regression

被引：0
|
作者：
Wen, Xuerong Meggie [1 ]
Setodji, C. Messan [2 ]
Adekpedjou, Akim [1 ]
机构：
[1] Missouri Univ Sci & Technol, Dept Math & Stat, Rolla, MO 65409 USA
[2] RAND Corp, Pittsburgh, PA 15213 USA
来源：
STATISTICS AND ITS INTERFACE | 2009年 / 2卷 / 04期
关键词：
Multivariate Regression; Dimension Reduction; Central Subspaces; Intra-cluster Information; k-means Clustering; ASYMPTOTICS;
D O I：
暂无
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We proposed a new method to estimate the intra-cluster adjusted central subspace for regressions with multivariate responses. Following Setodji and Cook (2004), we made use of the k-means algorithm to cluster the observed response vectors. Our method was designed to recover the intra-cluster information and outperformed previous method with respect to estimation accuracies on both the central subspace and its dimension. It also allowed us to test the predictor effects in a model-free approach. Simulation and a real data example were given to illustrate our methodology.
引用
收藏
页码：503 / 511
页数：9
相关论文
共 50 条
  • [1] Sufficient dimension reduction via inverse regression: A minimum discrepancy approach
    Cook, RD
    Ni, LQ
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2005, 100 (470) : 410 - 428
  • [2] K-means inverse regression
    Setodji, CM
    Cook, RD
    [J]. TECHNOMETRICS, 2004, 46 (04) : 421 - 429
  • [3] Functional k-means inverse regression
    Wang, Guochang
    Lin, Nan
    Zhang, Baoxue
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2014, 70 : 172 - 182
  • [4] Dimension Reduction via an Alternating Inverse Regression
    Zhu, Li-Ping
    Yin, Xiangrong
    Zhu, Li-Xing
    [J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2010, 19 (04) : 887 - 899
  • [5] Dimension reduction via parametric inverse regression
    Bura, E
    [J]. L(1)-STATISTICAL PROCEDURES AND RELATED TOPICS, 1997, 31 : 215 - 228
  • [6] A MINIMUM DISCREPANCY APPROACH WITH FOURIER TRANSFORM IN SUFFICIENT DIMENSION REDUCTION
    Weng, Jiaying
    Yin, Xiangrong
    [J]. STATISTICA SINICA, 2022, 32 : 2381 - 2403
  • [7] K-MEANS BASED APPROACH FOR OLAP DIMENSION UPDATES
    Bentayeb, Fadila
    [J]. ICEIS 2008: PROCEEDINGS OF THE TENTH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL DISI: DATABASES AND INFORMATION SYSTEMS INTEGRATION, 2008, : 531 - 534
  • [8] A New Dimension Reduction Method: Factor Discriminant K-means
    Roberto Rocci
    Stefano Antonio Gattone
    Maurizio Vichi
    [J]. Journal of Classification, 2011, 28 : 210 - 226
  • [9] A New Dimension Reduction Method: Factor Discriminant K-means
    Rocci, Roberto
    Gattone, Stefano Antonio
    Vichi, Maurizio
    [J]. JOURNAL OF CLASSIFICATION, 2011, 28 (02) : 210 - 226
  • [10] Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension
    Qian, Wei
    Ding, Shanshan
    Cook, R. Dennis
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2019, 114 (527) : 1277 - 1290
12345