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Probabilistic Principal Component Analysis

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Cited by:

  1. Michael T. Owyang & Jeremy Piger & Daniel Soques, 2022. "Contagious switching," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 415-432, March.
  2. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.
  3. Elizondo Rocío, 2013. "Forecasting the Term Structure of Interest Rates in Mexico Using an Affine Model," Working Papers 2013-03, Banco de México.
  4. Nong Jin & Shiyu Zhou, 2006. "Data‐driven variation source identification for manufacturing process using the eigenspace comparison method," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 383-396, August.
  5. Oriana Bandiera & Andrea Prat & Stephen Hansen & Raffaella Sadun, 2020. "CEO Behavior and Firm Performance," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1325-1369.
  6. Hron, K. & Templ, M. & Filzmoser, P., 2010. "Imputation of missing values for compositional data using classical and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3095-3107, December.
  7. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
  8. Paola Zuccolotto, 2012. "Principal component analysis with interval imputed missing values," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 1-23, January.
  9. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
  10. Serge Iovleff, 2015. "Probabilistic auto-associative models and semi-linear PCA," 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. 9(3), pages 267-286, September.
  11. Pablo Pereira Álvarez & Pierre Kerfriden & David Ryckelynck & Vincent Robin, 2021. "Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning," Mathematics, MDPI, vol. 9(18), pages 1-25, September.
  12. Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
  13. Fokoué, Ernest, 2005. "Mixtures of factor analyzers: an extension with covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 370-384, August.
  14. Gaofeng Jia & Alexandros Taflanidis & Norberto Nadal-Caraballo & Jeffrey Melby & Andrew Kennedy & Jane Smith, 2016. "Surrogate modeling for peak or time-dependent storm surge prediction over an extended coastal region using an existing database of synthetic storms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 909-938, March.
  15. Sabrina Duarte & Liliana Forzani & Pamela Llop & Rodrigo García Arancibia & Diego Tomassi, 2023. "Socioeconomic Index for Income and Poverty Prediction: A Sufficient Dimension Reduction Approach," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 318-346, June.
  16. Ivan De Boi & Carl Henrik Ek & Rudi Penne, 2023. "Surface Approximation by Means of Gaussian Process Latent Variable Models and Line Element Geometry," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
  17. Matteo Barigozzi, 2023. "Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models," Papers 2307.09864, arXiv.org, revised Jun 2024.
  18. Matteo Barigozzi & Matteo Luciani, 2019. "Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm," Papers 1910.03821, arXiv.org, revised Sep 2024.
  19. Qi Li & Yong Huang & Jiahui Chen & Xiaohui Liu & Xianghao Meng & Chao Lin, 2023. "Feature Selection and Damage Identification for Urban Railway Track Using Bayesian Globally Sparse Principal Component Analysis," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
  20. van der Linde, Angelika, 2008. "Variational Bayesian functional PCA," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 517-533, December.
  21. Matteo Barigozzi & Matteo Luciani, 2017. "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series 2017-111, Board of Governors of the Federal Reserve System (U.S.).
  22. Minkyung Kim & Sangdon Park & Joohyung Lee & Yongjae Joo & Jun Kyun Choi, 2017. "Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data," Energies, MDPI, vol. 10(10), pages 1-20, October.
  23. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
  24. Stefan Sommer, 2019. "An Infinitesimal Probabilistic Model for Principal Component Analysis of Manifold Valued Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 37-62, February.
  25. Gabriel Oliveira dos Santos & Esther Luna Colombini & Sandra Avila, 2022. "#PraCegoVer: A Large Dataset for Image Captioning in Portuguese," Data, MDPI, vol. 7(2), pages 1-27, January.
  26. Roman Švec & Kamil Pícha & Vivian L. White Baravalle Gilliam & Josef Navrátil & Hana Doležalová, 2012. "The impact of visitor segments on the perception of the quality of the product of accommodation establishments," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 60(7), pages 399-408.
  27. Dorota Toczydlowska & Gareth W. Peters & Man Chung Fung & Pavel V. Shevchenko, 2017. "Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components," Risks, MDPI, vol. 5(3), pages 1-77, July.
  28. Scano, Efisio Antonio & Grosso, Massimiliano & Pistis, Agata & Carboni, Gianluca & Cocco, Daniele, 2021. "An in-depth analysis of biogas production from locally agro-industrial by-products and residues. An Italian case," Renewable Energy, Elsevier, vol. 179(C), pages 308-318.
  29. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised May 2024.
  30. Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 3-40, March.
  31. Anish Agarwal & Munther Dahleh & Devavrat Shah & Dennis Shen, 2021. "Causal Matrix Completion," Papers 2109.15154, arXiv.org.
  32. Daniel Bartz & Kerr Hatrick & Christian W. Hesse & Klaus-Robert Muller & Steven Lemm, 2011. "Directional Variance Adjustment: improving covariance estimates for high-dimensional portfolio optimization," Papers 1109.3069, arXiv.org, revised Mar 2012.
  33. Lola Etiévant & Vivian Viallon, 2022. "On some limitations of probabilistic models for dimension‐reduction: Illustration in the case of probabilistic formulations of partial least squares," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(3), pages 331-346, August.
  34. Seppo Pulkkinen & Marko Mäkelä & Napsu Karmitsa, 2014. "A generative model and a generalized trust region Newton method for noise reduction," Computational Optimization and Applications, Springer, vol. 57(1), pages 129-165, January.
  35. Hugo Queiroz Abonizio & Janaina Ignacio de Morais & Gabriel Marques Tavares & Sylvio Barbon Junior, 2020. "Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features," Future Internet, MDPI, vol. 12(5), pages 1-18, May.
  36. Julie Josse & Jérôme Pagès & François Husson, 2011. "Multiple imputation in principal component analysis," 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. 5(3), pages 231-246, October.
  37. Frydman, Carola & Papanikolaou, Dimitris, 2018. "In search of ideas: Technological innovation and executive pay inequality," Journal of Financial Economics, Elsevier, vol. 130(1), pages 1-24.
  38. Benaych-Georges, Florent & Nadakuditi, Raj Rao, 2012. "The singular values and vectors of low rank perturbations of large rectangular random matrices," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 120-135.
  39. Ekvall, Karl Oskar, 2022. "Targeted principal components regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  40. James Ming Chen & Mira Zovko & Nika Šimurina & Vatroslav Zovko, 2021. "Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM 2.5 Pollution," IJERPH, MDPI, vol. 18(16), pages 1-59, August.
  41. Gaofeng Jia & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Jeffrey A. Melby & Andrew B. Kennedy & Jane M. Smith, 2016. "Surrogate modeling for peak or time-dependent storm surge prediction over an extended coastal region using an existing database of synthetic storms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 909-938, March.
  42. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Working Papers ECARES 2023-15, ULB -- Universite Libre de Bruxelles.
  43. Karina Gibert & Yaroslav Hernandez-Potiomkin, 2023. "A Unified Formal Framework for Factorial and Probabilistic Topic Modelling," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
  44. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
  45. Luca Ambrogioni & Marcel A J van Gerven & Eric Maris, 2017. "Dynamic decomposition of spatiotemporal neural signals," PLOS Computational Biology, Public Library of Science, vol. 13(5), pages 1-37, May.
  46. Monica Defend & Aleksey Min & Lorenzo Portelli & Franz Ramsauer & Francesco Sandrini & Rudi Zagst, 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data," Forecasting, MDPI, vol. 3(1), pages 1-35, February.
  47. Ligon, Ethan, 2017. "Estimating household welfare from disaggregate expenditures," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5gc4h1fm, Department of Agricultural & Resource Economics, UC Berkeley.
  48. Ruixu Zhou & Wensheng Gao & Weidong Liu & Dengwei Ding & Bowen Zhang, 2021. "Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS," Energies, MDPI, vol. 14(22), pages 1-27, November.
  49. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  50. Stefan Posch & Clemens Gößnitzer & Andreas B. Ofner & Gerhard Pirker & Andreas Wimmer, 2022. "Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder," Energies, MDPI, vol. 15(7), pages 1-16, March.
  51. Hirose, Kei & Fujisawa, Hironori & Sese, Jun, 2017. "Robust sparse Gaussian graphical modeling," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 172-190.
  52. Bork, Lasse & Kaltwasser, Pablo Rovira & Sercu, Piet, 2022. "Aggregation bias in tests of the commodity currency hypothesis," Journal of Banking & Finance, Elsevier, vol. 135(C).
  53. Joni Virta & Niko Lietzén & Henri Nyberg, 2024. "Robust signal dimension estimation via SURE," Statistical Papers, Springer, vol. 65(5), pages 3007-3038, July.
  54. Sébastien Loisel & Yoshio Takane, 2019. "Comparisons among several methods for handling missing data in principal component analysis (PCA)," 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. 13(2), pages 495-518, June.
  55. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
  56. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
  57. Chen, Tao & Martin, Elaine & Montague, Gary, 2009. "Robust probabilistic PCA with missing data and contribution analysis for outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3706-3716, August.
  58. Daniel Bartz & Kerr Hatrick & Christian W Hesse & Klaus-Robert Müller & Steven Lemm, 2013. "Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-14, July.
  59. Danushka Bollegala & Georgios Kontonatsios & Sophia Ananiadou, 2015. "A Cross-Lingual Similarity Measure for Detecting Biomedical Term Translations," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-28, June.
  60. Debashis Ghosh & Jeremy M. G. Taylor & Daniel J. Sargent, 2012. "Meta-analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling," Biometrics, The International Biometric Society, vol. 68(1), pages 226-232, March.
  61. Phuong T. Vu & Timothy V. Larson & Adam A. Szpiro, 2020. "Probabilistic predictive principal component analysis for spatially misaligned and high‐dimensional air pollution data with missing observations," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
  62. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
  63. Tilman M. Davies & Sudipto Banerjee & Adam P. Martin & Rose E. Turnbull, 2022. "A nearest‐neighbour Gaussian process spatial factor model for censored, multi‐depth geochemical data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 1014-1043, August.
  64. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
  65. Marconi, Gabriele, 2014. "European higher education policies and the problem of estimating a complex model with a small cross-section," MPRA Paper 87600, University Library of Munich, Germany.
  66. Junfan Mao & Zhigen Gao & Bing-Yi Jing & Jianhua Guo, 2024. "On the statistical analysis of high-dimensional factor models," Statistical Papers, Springer, vol. 65(8), pages 4991-5019, October.
  67. Aman Agrawal & Alec M Chiu & Minh Le & Eran Halperin & Sriram Sankararaman, 2020. "Scalable probabilistic PCA for large-scale genetic variation data," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-19, May.
  68. Kwame Adu-Gyamfi & Emmanuel Opoku, 2016. "Climate Change as an Emerging Component of Project Risk in the Agriculture Sector: An Empirical Assessment," International Business Research, Canadian Center of Science and Education, vol. 9(11), pages 215-221, November.
  69. Roger S. Zoh & Bani Mallick & Ivan Ivanov & Veera Baladandayuthapani & Ganiraju Manyam & Robert S. Chapkin & Johanna W. Lampe & Raymond J. Carroll, 2016. "PCAN: Probabilistic correlation analysis of two non‐normal data sets," Biometrics, The International Biometric Society, vol. 72(4), pages 1358-1368, December.
  70. Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  71. Jun Lu, 2022. "Bayesian Low-Rank Interpolative Decomposition for Complex Datasets," Studies in Engineering and Technology, Redfame publishing, vol. 9(1), pages 112-112, December.
  72. Cook, R. Dennis, 2022. "A slice of multivariate dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  73. Lin, Liang-Ching & Chen, Ray-Bing & Huang, Mong-Na Lo & Guo, Meihui, 2020. "Huber-type principal expectile component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
  74. Shah, Syed Muhammad Saqlain & Batool, Safeera & Khan, Imran & Ashraf, Muhammad Usman & Abbas, Syed Hussnain & Hussain, Syed Adnan, 2017. "Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 796-807.
  75. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
  76. Veronika Ročková & Edward I. George, 2016. "Fast Bayesian Factor Analysis via Automatic Rotations to Sparsity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1608-1622, October.
  77. 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).
  78. Andrés R. Masegosa & Darío Ramos-López & Antonio Salmerón & Helge Langseth & Thomas D. Nielsen, 2020. "Variational Inference over Nonstationary Data Streams for Exponential Family Models," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
  79. Erik Fisher & Catherine P. Slade & Derrick Anderson & Barry Bozeman, 2010. "The public value of nanotechnology?," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 29-39, October.
  80. Jung, WoongHee & Taflanidis, Alexandros A. & Kyprioti, Aikaterini P. & Zhang, Jize, 2024. "Adaptive multi-fidelity Monte Carlo for real-time probabilistic storm surge predictions," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
  81. Xing, Xin & Xie, Rui & Zhong, Wenxuan, 2022. "Model-based sparse coding beyond Gaussian independent model," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
  82. Zhao, Jianhua & Shi, Lei, 2014. "Automated learning of factor analysis with complete and incomplete data," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 205-218.
  83. el Bouhaddani, Said & Uh, Hae-Won & Hayward, Caroline & Jongbloed, Geurt & Houwing-Duistermaat, Jeanine, 2018. "Probabilistic partial least squares model: Identifiability, estimation and application," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 331-346.
  84. Girard, Stéphane & Iovleff, Serge, 2005. "Auto-associative models and generalized principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 21-39, March.
  85. Gen Li & Sungkyu Jung, 2017. "Incorporating covariates into integrated factor analysis of multi‐view data," Biometrics, The International Biometric Society, vol. 73(4), pages 1433-1442, December.
  86. Luke Smallman & William Underwood & Andreas Artemiou, 2020. "Simple Poisson PCA: an algorithm for (sparse) feature extraction with simultaneous dimension determination," Computational Statistics, Springer, vol. 35(2), pages 559-577, June.
  87. Li, Min & Wang, Ruo-Qian & Jia, Gaofeng, 2020. "Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  88. John B. Holmes & Matthew R. Schofield & Richard J. Barker, 2022. "Pólya‐gamma data augmentation and latent variable models for multivariate binomial data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 194-218, January.
  89. Hirose, Kei & Miura, Kanta & Koie, Atori, 2023. "Hierarchical clustered multiclass discriminant analysis via cross-validation," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
  90. Karl Friston, 2008. "Hierarchical Models in the Brain," PLOS Computational Biology, Public Library of Science, vol. 4(11), pages 1-24, November.
  91. Vegard Høghaug Larsen & Nicolò Maffei-Faccioli & Laura Pagenhardt, 2023. "Where do they care? The ECB in the media and inflation expectations," Working Papers No 04/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  92. Julia Wrobel & Vadim Zipunnikov & Jennifer Schrack & Jeff Goldsmith, 2019. "Registration for exponential family functional data," Biometrics, The International Biometric Society, vol. 75(1), pages 48-57, March.
  93. Afghari, Amir Pooyan & Faghih Imani, Ahmadreza & Papadimitriou, Eleonora & van Gelder, Pieter & Hezaveh, Amin Mohamadi, 2021. "Disentangling the effects of unobserved factors on seatbelt use choices in multi-occupant vehicles," Journal of choice modelling, Elsevier, vol. 41(C).
  94. Chianese, E. & Galletti, A. & Giunta, G. & Landi, T.C. & Marcellino, L. & Montella, R. & Riccio, A., 2018. "Spatiotemporally resolved ambient particulate matter concentration by fusing observational data and ensemble chemical transport model simulations," Ecological Modelling, Elsevier, vol. 385(C), pages 173-181.
  95. Jingying Yang, 2024. "Element Aggregation for Estimation of High-Dimensional Covariance Matrices," Mathematics, MDPI, vol. 12(7), pages 1-16, March.
  96. 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.
  97. Jaewook Lee & Mohamed Boubekri & Feng Liang, 2019. "Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique," Sustainability, MDPI, vol. 11(5), pages 1-21, March.
  98. Carlo Cavicchia & Maurizio Vichi & Giorgia Zaccaria, 2022. "Gaussian mixture model with an extended ultrametric covariance structure," 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. 16(2), pages 399-427, June.
  99. Brodmann, Jennifer & Hossain, Ashrafee & Masum, Abdullah Al & Singhvi, Meghna, 2021. "Chief Executive Officer power and Corporate Sexual Orientation Equality," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
  100. Wenlong Sun & Olfa Nasraoui & Patrick Shafto, 2020. "Evolution and impact of bias in human and machine learning algorithm interaction," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.
  101. Yi-Hao Kao & Benjamin Van Roy, 2014. "Directed Principal Component Analysis," Operations Research, INFORMS, vol. 62(4), pages 957-972, August.
  102. Jingtao Wang & Gregory J. Fonseca & Jun Ding, 2024. "scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning," Nature Communications, Nature, vol. 15(1), pages 1-27, December.
  103. Anish Agarwal & Devavrat Shah & Dennis Shen, 2020. "Synthetic Interventions," Papers 2006.07691, arXiv.org, revised Aug 2024.
  104. Vincent Vandewalle, 2020. "Multi-Partitions Subspace Clustering," Mathematics, MDPI, vol. 8(4), pages 1-18, April.
  105. Rajbir-Singh Nirwan & Nils Bertschinger, 2018. "Applications of Gaussian Process Latent Variable Models in Finance," Papers 1806.03294, arXiv.org, revised Apr 2019.
  106. Aaron Fisher & Brian Caffo & Brian Schwartz & Vadim Zipunnikov, 2016. "Fast, Exact Bootstrap Principal Component Analysis for > 1 Million," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 846-860, April.
  107. Xiaoran Huang & Demiao Yu, 2023. "Assessment of Regional Health Resource Carrying Capacity and Security in Public Health Emergencies Based on the COVID-19 Outbreak," IJERPH, MDPI, vol. 20(3), pages 1-27, January.
  108. Francesco Curreri & Giacomo Fiumara & Maria Gabriella Xibilia, 2020. "Input Selection Methods for Soft Sensor Design: A Survey," Future Internet, MDPI, vol. 12(6), pages 1-24, June.
  109. Wasito, Ito & Mirkin, Boris, 2006. "Nearest neighbours in least-squares data imputation algorithms with different missing patterns," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 926-949, February.
  110. Jeff Goldsmith & Vadim Zipunnikov & Jennifer Schrack, 2015. "Generalized multilevel function-on-scalar regression and principal component analysis," Biometrics, The International Biometric Society, vol. 71(2), pages 344-353, June.
  111. Dorota Toczydlowska & Gareth W. Peters, 2018. "Financial Big Data Solutions for State Space Panel Regression in Interest Rate Dynamics," Econometrics, MDPI, vol. 6(3), pages 1-45, July.
  112. Pedro Delicado & Mario Huerta, 2003. "Principal Curves of Oriented Points: theoretical and computational improvements," Computational Statistics, Springer, vol. 18(2), pages 293-315, July.
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