IDEAS home Printed from https://ideas.repec.org/r/eee/jmvana/v5y1975i2p248-264.html
   My bibliography  Save this item

Reduced-rank regression for the multivariate linear model

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Lansangan, Joseph Ryan G. & Barrios, Erniel B., 2017. "Simultaneous dimension reduction and variable selection in modeling high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 242-256.
  2. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
  3. Carlos Enrique Carrasco Gutiérrez & Reinaldo Castro Souza & Osmani Teixeira de Carvalho Guillén, 2007. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Working Papers Series 139, Central Bank of Brazil, Research Department.
  4. A. Mukherjee & K. Chen & N. Wang & J. Zhu, 2015. "On the degrees of freedom of reduced-rank estimators in multivariate regression," Biometrika, Biometrika Trust, vol. 102(2), pages 457-477.
  5. Forzani, Liliana & Rodriguez, Daniela & Smucler, Ezequiel & Sued, Mariela, 2019. "Sufficient dimension reduction and prediction in regression: Asymptotic results," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 339-349.
  6. Heungsun Hwang & Hye Suk & Jang-Han Lee & D. Moskowitz & Jooseop Lim, 2012. "Functional Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 524-542, July.
  7. Xing Gao & Sungwon Lee & Gen Li & Sungkyu Jung, 2021. "Covariate‐driven factorization by thresholding for multiblock data," Biometrics, The International Biometric Society, vol. 77(3), pages 1011-1023, September.
  8. An, Baiguo & Guo, Jianhua & Wang, Hansheng, 2013. "Multivariate regression shrinkage and selection by canonical correlation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 93-107.
  9. Hansen, Peter Reinhard, 2003. "Structural changes in the cointegrated vector autoregressive model," Journal of Econometrics, Elsevier, vol. 114(2), pages 261-295, June.
  10. Kargin, Vladislav, 2015. "On estimation in the reduced-rank regression with a large number of responses and predictors," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 377-394.
  11. Andrés García-Medina & Graciela González Farías, 2020. "Transfer entropy as a variable selection methodology of cryptocurrencies in the framework of a high dimensional predictive model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-31, January.
  12. Chao, Shih-Kang & Härdle, Wolfgang K. & Huang, Chen, 2018. "Multivariate factorizable expectile regression with application to fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 1-19.
  13. Chen, Rong & Xiao, Han & Yang, Dan, 2021. "Autoregressive models for matrix-valued time series," Journal of Econometrics, Elsevier, vol. 222(1), pages 539-560.
  14. Fujikoshi, Yasunori & Sakurai, Tetsuro, 2016. "High-dimensional consistency of rank estimation criteria in multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 199-212.
  15. Goh, Gyuhyeong & Dey, Dipak K. & Chen, Kun, 2017. "Bayesian sparse reduced rank multivariate regression," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 14-28.
  16. Emilie Devijver, 2017. "Model-based regression clustering for high-dimensional data: application to functional data," 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. 11(2), pages 243-279, June.
  17. Burkett, John P., 1998. "Bureaucratic behavior modeled by reduced-rank regression: The case of expenditures from the Soviet state budget," Journal of Economic Behavior & Organization, Elsevier, vol. 34(1), pages 173-187, January.
  18. Abby Israels, 1984. "Redundancy analysis for qualitative variables," Psychometrika, Springer;The Psychometric Society, vol. 49(3), pages 331-346, September.
  19. Gutierrez, Carlos Enrique Carrasco & Souza, Reinaldo Castro & Guillén, Osmani Teixeira de Carvalho, 2009. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 29(1), May.
  20. Gilbert, Scott & Zemcík, Petr, 2006. "Who's afraid of reduced-rank parameterizations of multivariate models? Theory and example," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 925-945, April.
  21. Tan, Zhixue & Zhong, Shisheng & Lin, Lin, 2019. "Trans-layer model learning: A hierarchical modeling strategy for real-time reliability evaluation of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 120-132.
  22. Yee, Thomas W., 2014. "Reduced-rank vector generalized linear models with two linear predictors," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 889-902.
  23. Pietro Giorgio Lovaglio & Giuseppe Folloni, 2011. "The estimation of Human Capital in structural models with flexible specification," Working Papers 11, AlmaLaurea Inter-University Consortium.
  24. Zhou, Guofu, 1995. "Small sample rank tests with applications to asset pricing," Journal of Empirical Finance, Elsevier, vol. 2(1), pages 71-93, March.
  25. Lian, Heng & Feng, Sanying & Zhao, Kaifeng, 2015. "Parametric and semiparametric reduced-rank regression with flexible sparsity," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 163-174.
  26. Shih-Kang Chao & Wolfgang K. Härdle & Chen Huang, 2016. "Multivariate Factorisable Sparse Asymmetric Least Squares Regression," SFB 649 Discussion Papers SFB649DP2016-058, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  27. Dong, Ruipeng & Li, Daoji & Zheng, Zemin, 2021. "Parallel integrative learning for large-scale multi-response regression with incomplete outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
  28. Anderson, T.W., 2006. "Reduced rank regression for blocks of simultaneous equations," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 55-76.
  29. Dmitry Kobak & Yves Bernaerts & Marissa A. Weis & Federico Scala & Andreas S. Tolias & Philipp Berens, 2021. "Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 980-1000, August.
  30. Kun Chen & Kung-Sik Chan & Nils Chr. Stenseth, 2014. "Source-Sink Reconstruction Through Regularized Multicomponent Regression Analysis-With Application to Assessing Whether North Sea Cod Larvae Contributed to Local Fjord Cod in Skagerrak," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 560-573, June.
  31. Lin, Hongmei & Jiang, Xuejun & Lian, Heng & Zhang, Weiping, 2019. "Reduced rank modeling for functional regression with functional responses," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 205-217.
  32. Cajo Braak, 1990. "Interpreting canonical correlation analysis through biplots of structure correlations and weights," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 519-531, September.
  33. Peter Reinhard Hansen, 2008. "Reduced-Rank Regression: A Useful Determinant Identity," CREATES Research Papers 2008-02, Department of Economics and Business Economics, Aarhus University.
  34. Heungsun Hwang & Yoshio Takane, 2004. "A multivariate reduced-rank growth curve model with unbalanced data," Psychometrika, Springer;The Psychometric Society, vol. 69(1), pages 65-79, March.
  35. Canhong Wen & Zhenduo Li & Ruipeng Dong & Yijin Ni & Wenliang Pan, 2023. "Simultaneous Dimension Reduction and Variable Selection for Multinomial Logistic Regression," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1044-1060, September.
  36. Yiyuan She & Jiahui Shen & Chao Zhang, 2022. "Supervised multivariate learning with simultaneous feature auto‐grouping and dimension reduction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 912-932, July.
  37. Luo, Ruiyan & Qi, Xin, 2017. "Signal extraction approach for sparse multivariate response regression," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 83-97.
  38. 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).
  39. Christian Grussler & Pontus Giselsson, 2022. "Efficient Proximal Mapping Computation for Low-Rank Inducing Norms," Journal of Optimization Theory and Applications, Springer, vol. 192(1), pages 168-194, January.
  40. GONZALO, Jesus & PITARAKIS, Jean-Yves, 1994. "Comovements in Large Systems," LIDAM Discussion Papers CORE 1994065, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  41. Ziping Zhao & Daniel P. Palomar, 2018. "Sparse Reduced Rank Regression With Nonconvex Regularization," Papers 1803.07247, arXiv.org.
  42. Maurizio Carpita & Paola Pasca & Serena Arima & Enrico Ciavolino, 2023. "Clustering of variables methods and measurement models for soccer players’ performances," Annals of Operations Research, Springer, vol. 325(1), pages 37-56, June.
  43. Fan, Jianqing & Gong, Wenyan & Zhu, Ziwei, 2019. "Generalized high-dimensional trace regression via nuclear norm regularization," Journal of Econometrics, Elsevier, vol. 212(1), pages 177-202.
  44. Wayne DeSarbo & Heungsun Hwang & Ashley Stadler Blank & Eelco Kappe, 2015. "Constrained Stochastic Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 516-534, June.
  45. Alain-Philippe Fortin & Patrick Gagliardini & Olivier Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Papers 2210.16042, arXiv.org.
  46. Shih-Kang Chao & Wolfgang K. Härdle & Ming Yuan, 2015. "Factorisable Sparse Tail Event Curves," SFB 649 Discussion Papers SFB649DP2015-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  47. Terry Elrod & Gerald Häubl & Steven Tipps, 2012. "Parsimonious Structural Equation Models for Repeated Measures Data, with Application to the Study of Consumer Preferences," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 358-387, April.
  48. Andr'es Garc'ia Medina & Graciela Gonz'alez-Far'ias, 2019. "Determining the number of factors in a forecast model by a random matrix test: cryptocurrencies," Papers 1905.00545, arXiv.org.
  49. Li, Gen & Yang, Dan & Nobel, Andrew B. & Shen, Haipeng, 2016. "Supervised singular value decomposition and its asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 7-17.
  50. Variyam, Jayachandran N. & Jordan, Jeffrey L., 1991. "Economic Perceptions And Agricultural Policy Preferences," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 16(2), pages 1-11, December.
  51. Peter Hansen, 2002. "Generalized Reduced Rank Regression," Working Papers 2002-02, Brown University, Department of Economics.
  52. D'Ambra, Luigi & Amenta, Pietro & D'Ambra, Antonello & de Tibeiro, Jules S., 2021. "A study of the family service expenditures and the socio-demographic characteristics via fixed marginals correspondence analysis," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
  53. Kohei Yoshikawa & Shuichi Kawano, 2023. "Sparse reduced-rank regression for simultaneous rank and variable selection via manifold optimization," Computational Statistics, Springer, vol. 38(1), pages 53-75, March.
  54. Hu, Jianhua & Liu, Xiaoqian & Liu, Xu & Xia, Ningning, 2022. "Some aspects of response variable selection and estimation in multivariate linear regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  55. Jung, Sungkyu, 2018. "Continuum directions for supervised dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 27-43.
  56. Lian, Heng & Kim, Yongdai, 2016. "Nonconvex penalized reduced rank regression and its oracle properties in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 383-393.
  57. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
  58. Bura, Efstathia & Cook, R. Dennis, 2003. "Rank estimation in reduced-rank regression," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 159-176, October.
  59. Pierre O. Boucher & Tian Wang & Laura Carceroni & Gary Kane & Krishna V. Shenoy & Chandramouli Chandrasekaran, 2023. "Initial conditions combine with sensory evidence to induce decision-related dynamics in premotor cortex," Nature Communications, Nature, vol. 14(1), pages 1-28, December.
  60. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
  61. Shinya Sugawara, 2022. "What composes desirable formal at-home elder care? An analysis for multiple service combinations," The Japanese Economic Review, Springer, vol. 73(2), pages 373-402, April.
  62. Ben Deaner, 2021. "Many Proxy Controls," Papers 2110.03973, arXiv.org.
  63. Boik, Robert J., 1998. "A Local Parameterization of Orthogonal and Semi-Orthogonal Matrices with Applications," Journal of Multivariate Analysis, Elsevier, vol. 67(2), pages 244-276, November.
  64. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
  65. Takane, Yoshio & Hwang, Heungsun, 2005. "An extended redundancy analysis and its applications to two practical examples," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 785-808, June.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.