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On Consistency and Sparsity for Principal Components Analysis in High Dimensions

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Cited by:

  1. Fan, Jianqing & Ke, Yuan & Wang, Kaizheng, 2020. "Factor-adjusted regularized model selection," Journal of Econometrics, Elsevier, vol. 216(1), pages 71-85.
  2. Badi H. Baltagi & Chihwa Kao & Fa Wang, 2017. "Asymptotic power of the sphericity test under weak and strong factors in a fixed effects panel data model," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 853-882, October.
  3. Puyi Fang & Zhaoxing Gao & Ruey S. Tsay, 2023. "Determination of the effective cointegration rank in high-dimensional time-series predictive regressions," Papers 2304.12134, arXiv.org, revised Apr 2023.
  4. Zhu, Ziwei & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional principal component analysis with heterogeneous missingness," LSE Research Online Documents on Economics 117647, London School of Economics and Political Science, LSE Library.
  5. Shih-Hao Huang & Hsin-Cheng Huang & Ruey S. Tsay & Guangming Pan, 2021. "Testing Independence Between Two Spatial Random Fields," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 161-179, June.
  6. Anna Bykhovskaya & Vadim Gorin, 2023. "High-Dimensional Canonical Correlation Analysis," Papers 2306.16393, arXiv.org, revised Aug 2023.
  7. Nikulin, Vladimir & Huang, Tian-Hsiang & Ng, Shu-Kay & Rathnayake, Suren I. & McLachlan, Geoffrey J., 2011. "A very fast algorithm for matrix factorization," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 773-782, July.
  8. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," ISER Discussion Paper 1053r, Institute of Social and Economic Research, Osaka University, revised Mar 2020.
  9. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
  10. Kristoffer H. Hellton & Magne Thoresen, 2017. "When and Why are Principal Component Scores a Good Tool for Visualizing High-dimensional Data?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 581-597, September.
  11. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
  12. Ahmed Abdul Quadeer & David Morales-Jimenez & Matthew R McKay, 2018. "Co-evolution networks of HIV/HCV are modular with direct association to structure and function," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-29, September.
  13. Davy Paindaveine & Julien Remy & Thomas Verdebout, 2017. "Testing for Principal Component Directions under Weak Identifiability," Working Papers ECARES ECARES 2017-37, ULB -- Universite Libre de Bruxelles.
  14. Wang, Shao-Hsuan & Huang, Su-Yun & Chen, Ting-Li, 2020. "On asymptotic normality of cross data matrix-based PCA in high dimension low sample size," Journal of Multivariate Analysis, Elsevier, vol. 175(C).
  15. Bai, Jushan & Liao, Yuan, 2012. "Efficient Estimation of Approximate Factor Models," MPRA Paper 41558, University Library of Munich, Germany.
  16. Joakim Westerlund, 2020. "A cross‐section average‐based principal components approach for fixed‐T panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 776-785, September.
  17. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
  18. Chen, Songxi, 2012. "Two Sample Tests for High Dimensional Covariance Matrices," MPRA Paper 46026, University Library of Munich, Germany.
  19. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
  20. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
  21. Asai, Manabu & McAleer, Michael, 2015. "Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance," Journal of Econometrics, Elsevier, vol. 189(2), pages 251-262.
  22. P. Navarro-Esteban & J. A. Cuesta-Albertos, 2021. "High-dimensional outlier detection using random projections," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 908-934, December.
  23. Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.
  24. Steland, Ansgar, 2020. "Testing and estimating change-points in the covariance matrix of a high-dimensional time series," Journal of Multivariate Analysis, Elsevier, vol. 177(C).
  25. Jianqing Fan & Yuan Liao & Martina Mincheva, 2013. "Large covariance estimation by thresholding principal orthogonal complements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
  26. Yata, Kazuyoshi & Aoshima, Makoto, 2013. "PCA consistency for the power spiked model in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 334-354.
  27. Lee Woojoo & Lee Donghwan & Lee Youngjo & Pawitan Yudi, 2011. "Sparse Canonical Covariance Analysis for High-throughput Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-24, July.
  28. Yoshimasa Uematsu & Takashi Yamagata, 2019. "Estimation of Weak Factor Models," DSSR Discussion Papers 96, Graduate School of Economics and Management, Tohoku University.
  29. Dey, Rounak & Lee, Seunggeun, 2019. "Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 145-164.
  30. Barigozzi, Matteo & Trapani, Lorenzo, 2020. "Sequential testing for structural stability in approximate factor models," Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
  31. Damien Passemier & Zhaoyuan Li & Jianfeng Yao, 2017. "On estimation of the noise variance in high dimensional probabilistic principal component analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 51-67, January.
  32. T. F. Cox & D. S. Arnold, 2018. "Simple components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 83-99, January.
  33. Bertrand Maillet & Sessi Tokpavi & Benoit Vaucher, 2013. "Minimum Variance Portfolio Optimisation under Parameter Uncertainty: A Robust Control Approach," EconomiX Working Papers 2013-28, University of Paris Nanterre, EconomiX.
  34. Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
  35. Rodney V. Fonseca & Aluísio Pinheiro, 2020. "Wavelet estimation of the dimensionality of curve time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1175-1204, October.
  36. Bando, Takuma & Sei, Tomonari & Yata, Kazuyoshi, 2022. "Consistency of the objective general index in high-dimensional settings," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  37. Yang, Yang & Yang, Yanrong & Shang, Han Lin, 2022. "Feature extraction for functional time series: Theory and application to NIR spectroscopy data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
  38. Ningning Xia & Zhidong Bai, 2019. "Convergence rate of eigenvector empirical spectral distribution of large Wigner matrices," Statistical Papers, Springer, vol. 60(3), pages 983-1015, June.
  39. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
  40. Konrad Furmańczyk, 2021. "Estimation of autocovariance matrices for high dimensional linear processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(4), pages 595-613, May.
  41. Guerra Urzola, Rosember & Van Deun, Katrijn & Vera, J. C. & Sijtsma, K., 2021. "A guide for sparse PCA : Model comparison and applications," Other publications TiSEM 4d35b931-7f49-444b-b92f-a, Tilburg University, School of Economics and Management.
  42. Dehan Kong & Joseph G. Ibrahim & Eunjee Lee & Hongtu Zhu, 2018. "FLCRM: Functional linear cox regression model," Biometrics, The International Biometric Society, vol. 74(1), pages 109-117, March.
  43. Jungjun Choi & Hyukjun Kwon & Yuan Liao, 2023. "Inference for Low-rank Models without Estimating the Rank," Papers 2311.16440, arXiv.org.
  44. Sung Hoon Choi & Donggyu Kim, 2022. "Large Volatility Matrix Analysis Using Global and National Factor Models," Papers 2208.12323, arXiv.org, revised Dec 2022.
  45. Liusha Yang & Matthew R. Mckay & Romain Couillet, 2018. "High-Dimensional MVDR Beamforming: Optimized Solutions Based on Spiked Random Matrix Models," Post-Print hal-01957672, HAL.
  46. Feher Kristen & Whelan James & Müller Samuel, 2011. "Assessing Modularity Using a Random Matrix Theory Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-34, September.
  47. Fan, Jianqing & Wang, Weichen & Zhong, Yiqiao, 2019. "Robust covariance estimation for approximate factor models," Journal of Econometrics, Elsevier, vol. 208(1), pages 5-22.
  48. Soloveychik, I. & Trushin, D., 2016. "Gaussian and robust Kronecker product covariance estimation: Existence and uniqueness," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 92-113.
  49. Vladimir Koltchinskii & Karim Lounici, 2017. "New Asymptotic Results in Principal Component Analysis," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 254-297, August.
  50. Lam, Clifford & Yao, Qiwei & Bathia, Neil, 2011. "Estimation of latent factors for high-dimensional time series," LSE Research Online Documents on Economics 31549, London School of Economics and Political Science, LSE Library.
  51. Hsiao, Cheng, 2018. "Panel models with interactive effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 645-673.
  52. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  53. Yacine Aït-Sahalia & Dacheng Xiu, 2019. "Principal Component Analysis of High-Frequency Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 287-303, January.
  54. Banerjee, Trambak & Mukherjee, Gourab & Radchenko, Peter, 2017. "Feature screening in large scale cluster analysis," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 191-212.
  55. Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  56. He, Yong & Zhang, Mingjuan & Zhang, Xinsheng & Zhou, Wang, 2020. "High-dimensional two-sample mean vectors test and support recovery with factor adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
  57. Shen, Dan & Shen, Haipeng & Marron, J.S., 2013. "Consistency of sparse PCA in High Dimension, Low Sample Size contexts," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 317-333.
  58. Landgraf, Andrew J. & Lee, Yoonkyung, 2020. "Dimensionality reduction for binary data through the projection of natural parameters," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
  59. Namvar, Ethan & Phillips, Blake & Pukthuanthong, Kuntara & Raghavendra Rau, P., 2016. "Do hedge funds dynamically manage systematic risk?," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 1-15.
  60. Mark J. Meyer & Brent A. Coull & Francesco Versace & Paul Cinciripini & Jeffrey S. Morris, 2015. "Bayesian function‐on‐function regression for multilevel functional data," Biometrics, The International Biometric Society, vol. 71(3), pages 563-574, September.
  61. Li, Weiming & Gao, Jing & Li, Kunpeng & Yao, Qiwei, 2016. "Modelling multivariate volatilities via latent common factors," LSE Research Online Documents on Economics 68121, London School of Economics and Political Science, LSE Library.
  62. Silin, Igor & Spokoiny, Vladimir, 2018. "Bayesian inference for spectral projectors of covariance matrix," IRTG 1792 Discussion Papers 2018-027, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  63. Kristoffer Herland Hellton & Magne Thoresen, 2014. "The Impact of Measurement Error on Principal Component Analysis," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1051-1063, December.
  64. Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
  65. 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).
  66. Yata, Kazuyoshi & Aoshima, Makoto, 2010. "Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2060-2077, October.
  67. Zhaoxing Gao & Ruey S. Tsay, 2020. "A Two-Way Transformed Factor Model for Matrix-Variate Time Series," Papers 2011.09029, arXiv.org.
  68. Ziwei Zhu & Tengyao Wang & Richard J. Samworth, 2022. "High‐dimensional principal component analysis with heterogeneous missingness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 2000-2031, November.
  69. Kim, Donggyu & Wang, Yazhen, 2016. "Sparse PCA-based on high-dimensional Itô processes with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 172-189.
  70. Peter Bentler & Jan Leeuw, 2011. "Factor Analysis via Components Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(3), pages 461-470, July.
  71. Tao, Minjing & Wang, Yahzen & Yao, Qiwei & Zou, Jian, 2011. "Large volatility matrix inference via combining low-frequency and high-frequency approaches," LSE Research Online Documents on Economics 39321, London School of Economics and Political Science, LSE Library.
  72. Lei Wang & Xin Liu & Yin Zhang, 2023. "A communication-efficient and privacy-aware distributed algorithm for sparse PCA," Computational Optimization and Applications, Springer, vol. 85(3), pages 1033-1072, July.
  73. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
  74. Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J., 2021. "Sparse estimation of dynamic principal components for forecasting high-dimensional time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1498-1508.
  75. Luo, Ruiyan & Qi, Xin, 2015. "Sparse wavelet regression with multiple predictive curves," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 33-49.
  76. Xinyi Zhong & Chang Su & Zhou Fan, 2022. "Empirical Bayes PCA in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 853-878, July.
  77. Hong, David & Balzano, Laura & Fessler, Jeffrey A., 2018. "Asymptotic performance of PCA for high-dimensional heteroscedastic data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 435-452.
  78. Fang, Kuangnan & Fan, Xinyan & Zhang, Qingzhao & Ma, Shuangge, 2018. "Integrative sparse principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 1-16.
  79. 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.
  80. Xi Luo, 2011. "Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix Estimation," Papers 1111.1133, arXiv.org, revised Mar 2013.
  81. Yi-Hao Kao & Benjamin Van Roy, 2014. "Directed Principal Component Analysis," Operations Research, INFORMS, vol. 62(4), pages 957-972, August.
  82. Nickolay Trendafilov, 2014. "From simple structure to sparse components: a review," Computational Statistics, Springer, vol. 29(3), pages 431-454, June.
  83. Zvi Bodie & Jérôme Detemple & Marcel Rindisbacher, 2009. "Life-Cycle Finance and the Design of Pension Plans," Annual Review of Financial Economics, Annual Reviews, vol. 1(1), pages 249-286, November.
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  85. Didong Li & Minerva Mukhopadhyay & David B. Dunson, 2022. "Efficient manifold approximation with spherelets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1129-1149, September.
  86. Ali Hamzenejad & Saeid Jafarzadeh Ghoushchi & Vahid Baradaran & Abbas Mardani, 2020. "A Robust Algorithm for Classification and Diagnosis of Brain Disease Using Local Linear Approximation and Generalized Autoregressive Conditional Heteroscedasticity Model," Mathematics, MDPI, vol. 8(8), pages 1-19, August.
  87. Lu, Hengzhen & Zhu, Xiaoyu & Wang, Jianli & Yick, Ho Yin, 2021. "Share pledge transactions as an investor sentiment indicator - Evidence from China," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 230-238.
  88. Rosember Guerra-Urzola & Katrijn Van Deun & Juan C. Vera & Klaas Sijtsma, 2021. "A Guide for Sparse PCA: Model Comparison and Applications," Psychometrika, Springer;The Psychometric Society, vol. 86(4), pages 893-919, December.
  89. Gong, Tingnan & Zhang, Weiping & Chen, Yu, 2023. "Uncovering block structures in large rectangular matrices," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
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  91. Qi, Xin & Luo, Ruiyan & Zhao, Hongyu, 2013. "Sparse principal component analysis by choice of norm," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 127-160.
  92. Jesús Arroyo & Elizaveta Levina, 2022. "Overlapping Community Detection in Networks via Sparse Spectral Decomposition," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 1-35, June.
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