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Sparse partial least squares regression for simultaneous dimension reduction and variable selection

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Cited by:

  1. Yu, Dengdeng & Zhang, Li & Mizera, Ivan & Jiang, Bei & Kong, Linglong, 2019. "Sparse wavelet estimation in quantile regression with multiple functional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 12-29.
  2. Debamita Kundu & Riten Mitra & Jeremy T. Gaskins, 2021. "Bayesian variable selection for multioutcome models through shared shrinkage," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 295-320, March.
  3. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
  4. Cubadda, Gianluca & Guardabascio, Barbara, 2012. "A medium-N approach to macroeconomic forecasting," Economic Modelling, Elsevier, vol. 29(4), pages 1099-1105.
  5. Rory Samuels & Nimrod Carmon & Bledar Konomi & Jonathan Hobbs & Amy Braverman & Dean Young & Joon Jin Song, 2025. "Estimation of Impact Ranges for Functional Valued Predictors," Environmetrics, John Wiley & Sons, Ltd., vol. 36(5), July.
  6. Shan Luo, 2020. "Variable selection in high-dimensional sparse multiresponse linear regression models," Statistical Papers, Springer, vol. 61(3), pages 1245-1267, June.
  7. Ren, Shoujia & Guo, Bin & Wang, Zhijun & Wang, Juan & Fang, Quanxiao & Wang, Jianlin, 2022. "Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress," Agricultural Water Management, Elsevier, vol. 261(C).
  8. Bai, Ray & Ghosh, Malay, 2018. "High-dimensional multivariate posterior consistency under global–local shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 157-170.
  9. Tommy Löfstedt, 2025. "Using the Krylov subspace formulation to improve regularisation and interpretation in partial least squares regression," Computational Statistics, Springer, vol. 40(3), pages 1621-1642, March.
  10. R. D. Cook & I. S. Helland & Z. Su, 2013. "Envelopes and partial least squares regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(5), pages 851-877, November.
  11. Jaturong Som-ard & Savittri Ratanopad Suwanlee & Dusadee Pinasu & Surasak Keawsomsee & Kemin Kasa & Nattawut Seesanhao & Sarawut Ninsawat & Enrico Borgogno-Mondino & Filippo Sarvia, 2024. "Evaluating Sugarcane Yield Estimation in Thailand Using Multi-Temporal Sentinel-2 and Landsat Data Together with Machine-Learning Algorithms," Land, MDPI, vol. 13(9), pages 1-19, September.
  12. Jasmit Shah & Somnath Datta & Susmita Datta, 2014. "A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics," Computational Statistics, Springer, vol. 29(6), pages 1749-1767, December.
  13. Guo, Wenxing & Balakrishnan, Narayanaswamy & He, Mu, 2023. "Envelope-based sparse reduced-rank regression for multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
  14. David J. Olive, 2025. "Some Useful Techniques for High-Dimensional Statistics," Stats, MDPI, vol. 8(3), pages 1-15, July.
  15. Francesca R. Querdasi & Jessica P. Uy & Jennifer S. Labus & Jia Xu & Neerja Karnani & Ai Peng Tan & Birit B. F. P. Broekman & Peter D. Gluckman & Yap Seng Chong & Helen Chen & Marielle V. Fortier & Lo, 2025. "Childhood gut microbiome is linked to internalizing symptoms at school age via the functional connectome," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  16. Iason Kynigakis & Ekaterini Panopoulou, 2022. "Does model complexity add value to asset allocation? Evidence from machine learning forecasting models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 603-639, April.
  17. Luo, Ruiyan & Qi, Xin, 2017. "Signal extraction approach for sparse multivariate response regression," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 83-97.
  18. Firuz Kamalov & Hana Sulieman & Ayman Alzaatreh & Maher Emarly & Hasna Chamlal & Murodbek Safaraliev, 2025. "Mathematical Methods in Feature Selection: A Review," Mathematics, MDPI, vol. 13(6), pages 1-29, March.
  19. Stamer, Vincent, 2024. "Thinking outside the container: A sparse partial least squares approach to forecasting trade flows," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1336-1358.
  20. An, Baiguo & Zhang, Beibei, 2017. "Simultaneous selection of predictors and responses for high dimensional multivariate linear regression," Statistics & Probability Letters, Elsevier, vol. 127(C), pages 173-177.
  21. Maria Nareklishvili & Nicholas Polson & Vadim Sokolov, 2022. "Feature Selection for Personalized Policy Analysis," Papers 2301.00251, arXiv.org, revised Jul 2023.
  22. 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.
  23. Zhang, Ruoyang & Ghosh, Malay, 2022. "Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
  24. Jan J. J. Groen & Michael Nattinger, 2020. "Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression," Economic Policy Review, Federal Reserve Bank of New York, vol. 26(4), pages 39-68, October.
  25. Diego Ardila & Dorsa Sanadgol & Peter Cauwels & Didier Sornette, 2017. "Identification and critical time forecasting of real estate bubbles in the USA," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 613-631, April.
  26. Vahid Habibi & Hasan Ahmadi & Mohammad Jafari & Abolfazl Moeini, 2019. "Application of nonlinear models and groundwater index to predict desertification case study: Sharifabad watershed," 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. 99(2), pages 715-733, November.
  27. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
  28. 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.
  29. Kapetanios, George & Price, Simon & Young, Garry, 2018. "A UK financial conditions index using targeted data reduction: Forecasting and structural identification," Econometrics and Statistics, Elsevier, vol. 7(C), pages 1-17.
  30. 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.
  31. Kei Hirose & Miyuki Imada, 2018. "Sparse factor regression via penalized maximum likelihood estimation," Statistical Papers, Springer, vol. 59(2), pages 633-662, June.
  32. Emmanuel Jordy Menvouta & Sven Serneels & Tim Verdonck, 2022. "Sparse dimension reduction based on energy and ball statistics," 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(4), pages 951-975, December.
  33. Yongshuai Chen & Baosheng Liang, 2025. "Sure Independence Screening for Ultrahigh-Dimensional Additive Model with Multivariate Response," Mathematics, MDPI, vol. 13(10), pages 1-17, May.
  34. Christian Gayer & Alessandro Girardi & Andreas Reuter, 2016. "Replacing Judgment by Statistics: Constructing Consumer Confidence Indicators on the basis of Data-driven Techniques. The Case of the Euro Area," Working Papers LuissLab 16125, Dipartimento di Economia e Finanza, LUISS Guido Carli.
  35. Qiang Sun & Hongtu Zhu & Yufeng Liu & Joseph G. Ibrahim, 2015. "SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 289-302, March.
  36. 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.
  37. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
  38. 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.
  39. Shin, Seung Jun & Artemiou, Andreas, 2017. "Penalized principal logistic regression for sparse sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 48-58.
  40. Julieta Fuentes & Pilar Poncela & Julio Rodríguez, 2015. "Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 576-595, June.
  41. Lasanthi C. R. Pelawa Watagoda & David J. Olive, 2021. "Comparing six shrinkage estimators with large sample theory and asymptotically optimal prediction intervals," Statistical Papers, Springer, vol. 62(5), pages 2407-2431, October.
  42. Stamer, Vincent, 2022. "Thinking Outside the Container: A Sparse Partial Least Squares Approach to Forecasting Trade Flows," VfS Annual Conference 2022 (Basel): Big Data in Economics 264096, Verein für Socialpolitik / German Economic Association.
  43. Arief Gusnanto & Yudi Pawitan, 2015. "Sparse alternatives to ridge regression: a random effects approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(1), pages 12-26, January.
  44. 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.
  45. Groen, Jan J.J. & Kapetanios, George, 2016. "Revisiting useful approaches to data-rich macroeconomic forecasting," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 221-239.
  46. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
  47. Hayashi Takeshi, 2012. "Variational Bayes Procedure for Effective Classification of Tumor Type with Microarray Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-21, October.
  48. Luo, Ruiyan & Qi, Xin, 2015. "Sparse wavelet regression with multiple predictive curves," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 33-49.
  49. Golia Shafiei & Ben D. Fulcher & Bradley Voytek & Theodore D. Satterthwaite & Sylvain Baillet & Bratislav Misic, 2023. "Neurophysiological signatures of cortical micro-architecture," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  50. Bin Li & Hyunjin Shin & Georgy Gulbekyan & Olga Pustovalova & Yuri Nikolsky & Andrew Hope & Marina Bessarabova & Matthew Schu & Elona Kolpakova-Hart & David Merberg & Andrew Dorner & William L Trepicc, 2015. "Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.
  51. Shengkun Xie & Rebecca Luo, 2022. "Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
  52. 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.
  53. 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.
  54. 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.
  55. Cui, Jingyu & Yi, Grace Y., 2024. "Variable selection in multivariate regression models with measurement error in covariates," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
  56. Shih-Chi Peng & Chun-Ta Liao & Chien-Hua Peng & Ann-Joy Cheng & Shu-Jen Chen & Chung-Guei Huang & Wen-Ping Hsieh & Tzu-Chen Yen, 2014. "MicroRNAs MiR-218, MiR-125b, and Let-7g Predict Prognosis in Patients with Oral Cavity Squamous Cell Carcinoma," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-9, July.
  57. Zhang Yuping & Tibshirani Robert J. & Davis Ronald W., 2010. "Predicting Patient Survival from Longitudinal Gene Expression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-23, November.
  58. Minji Lee & Zhihua Su, 2020. "A Review of Envelope Models," International Statistical Review, International Statistical Institute, vol. 88(3), pages 658-676, December.
  59. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
  60. Hua Yun Chen & Hesen Li & Maria Argos & Victoria W. Persky & Mary E. Turyk, 2022. "Statistical Methods for Assessing the Explained Variation of a Health Outcome by a Mixture of Exposures," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
  61. Yingying Zhang & Volodymyr Melnykov & Igor Melnykov, 2023. "On Model-Based Clustering of Directional Data with Heavy Tails," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 527-551, November.
  62. Alvaro Mendez-Civieta & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2021. "Adaptive sparse group LASSO in quantile regression," 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. 15(3), pages 547-573, September.
  63. Martin, Manon & Govaerts, Bernadette, 2019. "Feature Selection in metabolomics with PLS-derived methods," LIDAM Discussion Papers ISBA 2019020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  64. Shuichi Kawano, 2021. "Sparse principal component regression via singular value decomposition approach," 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. 15(3), pages 795-823, September.
  65. repec:plo:pone00:0041694 is not listed on IDEAS
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