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Prediction by Supervised Principal Components

Citations

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Cited by:

  1. Massacci, Daniele & Kapetanios, George, 2024. "Forecasting in factor augmented regressions under structural change," International Journal of Forecasting, Elsevier, vol. 40(1), pages 62-76.
  2. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
  3. Jianqing Fan & Yang Feng & Jiancheng Jiang & Xin Tong, 2016. "Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 275-287, March.
  4. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
  5. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
  6. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
  7. Françoise Charpin, 2009. "Estimation précoce de la croissance," SciencePo Working papers Main hal-03476082, HAL.
  8. Cheng, Cheng, 2009. "Internal validation inferences of significant genomic features in genome-wide screening," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 788-800, January.
  9. Hyonho Chun & Sündüz Keleş, 2010. "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 3-25, January.
  10. Lorenzo Boldrini & Eric Hillebrand, 2015. "The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach," CREATES Research Papers 2015-39, Department of Economics and Business Economics, Aarhus University.
  11. Tommaso Proietti, 2016. "On the Selection of Common Factors for Macroeconomic Forecasting," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 593-628, Emerald Group Publishing Limited.
  12. Qiuyi Zhang & Yang Zhao & Ruyang Zhang & Yongyue Wei & Honggang Yi & Fang Shao & Feng Chen, 2016. "A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
  13. Emile du Plessis & Ulrich Fritsche, 2025. "New forecasting methods for an old problem: Predicting 147 years of systemic financial crises," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 3-40, January.
  14. 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.
  15. Klaus Abberger, 2007. "Forecasting Quarter-on-Quarter Changes of German GDP with Monthly Business Tendency Survey Results," ifo Working Paper Series 40, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  16. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
  17. Zambom, Adriano Zanin & Akritas, Michael G., 2015. "Nonparametric significance testing and group variable selection," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 51-60.
  18. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
  19. Koch, Inge & Naito, Kanta, 2010. "Prediction of multivariate responses with a selected number of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1791-1807, July.
  20. Lore Zumeta-Olaskoaga & Maximilian Weigert & Jon Larruskain & Eder Bikandi & Igor Setuain & Josean Lekue & Helmut Küchenhoff & Dae-Jin Lee, 2023. "Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 101-126, March.
  21. Philippe Schmitt-Kopplin & Norbert Hertkorn & Mourad Harir & Franco Moritz & Marianna Lucio & Lydie Bonal & Eric Quirico & Yoshinori Takano & Jason P. Dworkin & Hiroshi Naraoka & Shogo Tachibana & Tom, 2023. "Soluble organic matter Molecular atlas of Ryugu reveals cold hydrothermalism on C-type asteroid parent body," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  22. Eric Hillebrand & Tae-Hwy Lee, 2012. "Stein-Rule Estimation and Generalized Shrinkage Methods for Forecasting Using Many Predictors," Advances in Econometrics, in: 30th Anniversary Edition, pages 171-196, Emerald Group Publishing Limited.
  23. Yu Takagi & Hirokazu Matsuda & Yukio Taniguchi & Hiroaki Iwaisaki, 2014. "Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-17, December.
  24. Hansheng Wang & Chih‐Ling Tsai, 2009. "‘Model selection for generalized linear models with factor‐augmented predictors’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 241-242, May.
  25. Hatem Jemmali & Mohamed Salah Matoussi, 2012. "A Multidimensional Analysis of Water Poverty at A Local Scale- Application of Improved Water Poverty Index for Tunisia," Working Papers 730, Economic Research Forum, revised 2012.
  26. Gaynor, Sheila & Bair, Eric, 2017. "Identification of relevant subtypes via preweighted sparse clustering," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 139-154.
  27. Randy Carter & Netsanet Michael, 2022. "Factor Analysis Regression for Predictive Modeling with High-Dimensional Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 115-132, September.
  28. Claudio Conversano & Luca Frigau & Giulia Contu, 2024. "Overlapping coefficient in network-based semi-supervised clustering," Computational Statistics, Springer, vol. 39(7), pages 3831-3854, December.
  29. Axel Groß-Klußmann, 2024. "Learning deep news sentiment representations for macro-finance," Digital Finance, Springer, vol. 6(3), pages 341-377, September.
  30. Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
  31. Kui Shen & Nan Song & Youngchul Kim & Chunqiao Tian & Shara D Rice & Michael J Gabrin & W Fraser Symmans & Lajos Pusztai & Jae K Lee, 2012. "A Systematic Evaluation of Multi-Gene Predictors for the Pathological Response of Breast Cancer Patients to Chemotherapy," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
  32. Ekvall, Karl Oskar, 2022. "Targeted principal components regression," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
  33. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
  34. Xiuli Du & Xiaohu Jiang & Jinguan Lin, 2023. "Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 975-1001, September.
  35. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
  36. 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.
  37. Abberger, Klaus & Graff, Michael & Siliverstovs, Boriss & Sturm, Jan-Egbert, 2018. "Using rule-based updating procedures to improve the performance of composite indicators," Economic Modelling, Elsevier, vol. 68(C), pages 127-144.
  38. Gao, Yuhe & Shi, Chengchun & Song, Rui, 2023. "Deep spectral Q-learning with application to mobile health," LSE Research Online Documents on Economics 119445, London School of Economics and Political Science, LSE Library.
  39. Jialing Huang & Yihang Li & Yu Shi & Lihong Wang & Qing Zhou & Xiaohua Huang, 2019. "Effects of nutrient level and planting density on population relationship in soybean and wheat intercropping populations," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-12, December.
  40. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.
  41. Travaglini, Guido, 2011. "Principal Components and Factor Analysis. A Comparative Study," MPRA Paper 35486, University Library of Munich, Germany.
  42. Michael Graff & Dominik Studer, 2018. "Konstruktion von Sammelindikatoren für den Konjunkturverlauf bei prekärer Datenlage am Beispiel Montenegros," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 12(3), pages 81-91, October.
  43. Peter Bickel & Bo Li & Alexandre Tsybakov & Sara Geer & Bin Yu & Teófilo Valdés & Carlos Rivero & Jianqing Fan & Aad Vaart, 2006. "Regularization in statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(2), pages 271-344, September.
  44. Ng Serena & Bai Jushan, 2009. "Selecting Instrumental Variables in a Data Rich Environment," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-34, April.
  45. Xu-Qing Liu & Xiao-Cai Wang & Li Tao & Feng-Xian An & Gui-Ren Jiang, 2024. "Alleviating conditional independence assumption of naive Bayes," Statistical Papers, Springer, vol. 65(5), pages 2835-2863, July.
  46. Juan C. Laria & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2023. "Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models," Statistical Papers, Springer, vol. 64(1), pages 227-253, February.
  47. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.
  48. Marcella Michela Giuliani & Eugenio Nardella & Anna Gagliardi & Giuseppe Gatta, 2017. "Deficit Irrigation and Partial Root-Zone Drying Techniques in Processing Tomato Cultivated under Mediterranean Climate Conditions," Sustainability, MDPI, vol. 9(12), pages 1-15, November.
  49. Françoise Charpin, 2009. "Estimation précoce de la croissance," Post-Print hal-03476082, HAL.
  50. Zhenzhong Wang & Zhengyuan Zhu & Cindy Yu, 2020. "Variable Selection in Macroeconomic Forecasting with Many Predictors," Papers 2007.10160, arXiv.org.
  51. Wang Zhu & Wang C.Y., 2010. "Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
  52. Wei Zhang & Takayo Ota & Viji Shridhar & Jeremy Chien & Baolin Wu & Rui Kuang, 2013. "Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-16, March.
  53. Hojin Yang & Hongtu Zhu & Joseph G. Ibrahim, 2018. "MILFM: Multiple index latent factor model based on high‐dimensional features," Biometrics, The International Biometric Society, vol. 74(3), pages 834-844, September.
  54. Luis A. Barboza & Julien Emile-Geay & Bo Li & Wan He, 2019. "Efficient Reconstructions of Common Era Climate via Integrated Nested Laplace Approximations," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 535-554, September.
  55. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
  56. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.
  57. Erlyn K Macarayan & Hannah L Ratcliffe & Easmon Otupiri & Lisa R Hirschhorn & Kate Miller & Stuart R Lipsitz & Atul A Gawande & Asaf Bitton, 2019. "Facility management associated with improved primary health care outcomes in Ghana," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-19, July.
  58. repec:hal:spmain:info:hdl:2441/5l6uh8ogmqildh09h61q8alqn is not listed on IDEAS
  59. Hongtu Zhu & Dan Shen & Xuewei Peng & Leo Yufeng Liu, 2017. "MWPCR: Multiscale Weighted Principal Component Regression for High-Dimensional Prediction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1009-1021, July.
  60. Nguyen Tuan S & Rojo Javier, 2009. "Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-40, January.
  61. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  62. Federico Pavone & Juho Piironen & Paul-Christian Bürkner & Aki Vehtari, 2023. "Using reference models in variable selection," Computational Statistics, Springer, vol. 38(1), pages 349-371, March.
  63. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
  64. Anish Agarwal & Keegan Harris & Justin Whitehouse & Zhiwei Steven Wu, 2023. "Adaptive Principal Component Regression with Applications to Panel Data," Papers 2307.01357, arXiv.org, revised Aug 2024.
  65. Wan, Runzhe & Li, Yingying & Lu, Wenbin & Song, Rui, 2024. "Mining the factor zoo: Estimation of latent factor models with sufficient proxies," Journal of Econometrics, Elsevier, vol. 239(2).
  66. Yichen Cheng & Xinlei Wang & Yusen Xia, 2021. "Supervised t -Distributed Stochastic Neighbor Embedding for Data Visualization and Classification," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 566-585, May.
  67. Luke Hartigan & Tom Rosewall, 2024. "Nowcasting Quarterly GDP Growth during the COVID-19 Crisis Using a Monthly Activity Indicator," Working Papers 2024-15, University of Sydney, School of Economics.
  68. van Wieringen, Wessel N. & Kun, David & Hampel, Regina & Boulesteix, Anne-Laure, 2009. "Survival prediction using gene expression data: A review and comparison," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1590-1603, March.
  69. Antoniadis, Anestis & Fryzlewicz, Piotr & Letué, Frédérique, 2010. "The Dantzig selector in Cox's proportional hazards model," LSE Research Online Documents on Economics 30992, London School of Economics and Political Science, LSE Library.
  70. Min Cai & Hui Dai & Yongyong Qiu & Yang Zhao & Ruyang Zhang & Minjie Chu & Juncheng Dai & Zhibin Hu & Hongbing Shen & Feng Chen, 2013. "SNP Set Association Analysis for Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
  71. Zemin Zheng & Jinchi Lv & Wei Lin, 2021. "Nonsparse Learning with Latent Variables," Operations Research, INFORMS, vol. 69(1), pages 346-359, January.
  72. Huang, Dashan & Jiang, Fuwei & Li, Kunpeng & Tong, Guoshi & Zhou, Guofu, 2023. "Are bond returns predictable with real-time macro data?," Journal of Econometrics, Elsevier, vol. 237(2).
  73. Chen, Xi & Wang, Lily & Ishwaran, Hemant, 2010. "An integrative pathway-based clinical-genomic model for cancer survival prediction," Statistics & Probability Letters, Elsevier, vol. 80(17-18), pages 1313-1319, September.
  74. Luo, Ruiyan & Qi, Xin, 2015. "Sparse wavelet regression with multiple predictive curves," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 33-49.
  75. Anestis Antoniadis & Piotr Fryzlewicz & Frédérique Letué, 2010. "The Dantzig Selector in Cox's Proportional Hazards Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 531-552, December.
  76. Kim Kaivanto & Peng Zhang, 2019. "Investor Sentiment as a Predictor of Market Returns," Working Papers 268005798, Lancaster University Management School, Economics Department.
  77. Kawano, Shuichi & Fujisawa, Hironori & Takada, Toyoyuki & Shiroishi, Toshihiko, 2015. "Sparse principal component regression with adaptive loading," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 192-203.
  78. Palzer, Elise F. & Wendt, Christine H. & Bowler, Russell P. & Hersh, Craig P. & Safo, Sandra E. & Lock, Eric F., 2022. "sJIVE: Supervised joint and individual variation explained," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
  79. Anish Agarwal & Devavrat Shah & Dennis Shen, 2020. "Synthetic Interventions," Papers 2006.07691, arXiv.org, revised Aug 2024.
  80. Ma, Chenjie & Menke, Jan-Hendrik & Dasenbrock, Johannes & Braun, Martin & Haslbeck, Matthias & Schmid, Karl-Heinz, 2019. "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, Elsevier, vol. 256(C).
  81. 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.
  82. Heij, C., 2007. "Improved forecasting with leading indicators: the principal covariate index," Econometric Institute Research Papers EI 2007-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  83. repec:spo:wpmain:info:hdl:2441/5l6uh8ogmqildh09h61q8alqn is not listed on IDEAS
  84. Jing-Zhi Huang & Zhan Shi, 2023. "Machine-Learning-Based Return Predictors and the Spanning Controversy in Macro-Finance," Management Science, INFORMS, vol. 69(3), pages 1780-1804, March.
  85. Gao, Guangyuan & Wüthrich, Mario V. & Yang, Hanfang, 2019. "Evaluation of driving risk at different speeds," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 108-119.
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