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Combining eigenvalues and variation of eigenvectors for order determination

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Cited by:

  1. Zhang, Xin & Wang, Chong & Wu, Yichao, 2018. "Functional envelope for model-free sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 37-50.
  2. Kapla, Daniel & Fertl, Lukas & Bura, Efstathia, 2022. "Fusing sufficient dimension reduction with neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
  3. Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  4. Virta, Joni, 2021. "Testing for subsphericity when n and p are of different asymptotic order," Statistics & Probability Letters, Elsevier, vol. 179(C).
  5. Lu Li & Kai Tan & Xuerong Meggie Wen & Zhou Yu, 2023. "Variable-dependent partial dimension reduction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 521-541, June.
  6. Dong, Yushen & Wu, Yichao, 2022. "Fréchet kernel sliced inverse regression," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
  7. Qin Wang & Yuan Xue, 2023. "A structured covariance ensemble for sufficient dimension reduction," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 777-800, September.
  8. Hayley Randall & Andreas Artemiou & Xingye Qiao, 2021. "Sufficient dimension reduction based on distance‐weighted discrimination," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1186-1211, December.
  9. Nordhausen, Klaus & Ruiz-Gazen, Anne, 2022. "On the usage of joint diagonalization in multivariate statistics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  10. Wang, Qin & Xue, Yuan, 2021. "An ensemble of inverse moment estimators for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
  11. Song, Jun & Kim, Kyongwon & Yoo, Jae Keun, 2023. "On a nonlinear extension of the principal fitted component model," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
  12. Seungchul Baek & Yen‐Yi Ho & Yanyuan Ma, 2020. "Using sufficient direction factor model to analyze latent activities associated with breast cancer survival," Biometrics, The International Biometric Society, vol. 76(4), pages 1340-1350, December.
  13. Kim, Kyongwon, 2022. "On principal graphical models with application to gene network," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
  14. Klaus Nordhausen & Anne Ruiz-Gazen, 2022. "On the usage of joint diagonalization in multivariate statistics," Post-Print hal-04296111, HAL.
  15. Pircalabelu, Eugen & Artemiou, Andreas, 2021. "Graph informed sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
  16. Chen, Fei & Shi, Lei & Zhu, Xuehu & Zhu, Lixing, 2018. "Generalized principal Hessian directions for mixture multivariate skew elliptical distributions," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 142-159.
  17. Yuefeng Han & Rong Chen & Cun-Hui Zhang, 2020. "Rank Determination in Tensor Factor Model," Papers 2011.07131, arXiv.org, revised May 2022.
  18. Zhu, Xuehu & Guo, Xu & Wang, Tao & Zhu, Lixing, 2020. "Dimensionality determination: A thresholding double ridge ratio approach," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
  19. Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2022. "Asymptotic and bootstrap tests for subspace dimension," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  20. Fang, Fang & Yu, Zhou, 2020. "Model averaging assisted sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
  21. Zeng, Yicheng & Zhu, Lixing, 2023. "Order determination for spiked-type models with a divergent number of spikes," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
  22. Xie, Chuanlong & Zhu, Lixing, 2020. "Generalized kernel-based inverse regression methods for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
  23. Shih‐Hao Huang & Kerby Shedden & Hsin‐wen Chang, 2023. "Inference for the dimension of a regression relationship using pseudo‐covariates," Biometrics, The International Biometric Society, vol. 79(3), pages 2394-2403, September.
  24. Wang, Pei & Yin, Xiangrong & Yuan, Qingcong & Kryscio, Richard, 2021. "Feature filter for estimating central mean subspace and its sparse solution," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
  25. Stephen Babos & Andreas Artemiou, 2020. "Sliced inverse median difference regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 937-954, December.
  26. Wei Luo, 2022. "On efficient dimension reduction with respect to the interaction between two response variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 269-294, April.
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