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Regularization Paths for Generalized Linear Models via Coordinate Descent

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  1. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
  2. Felix Pretis & Lea Schneider & Jason E. Smerdon & David F. Hendry, 2016. "Detecting Volcanic Eruptions In Temperature Reconstructions By Designed Break-Indicator Saturation," Journal of Economic Surveys, Wiley Blackwell, vol. 30(3), pages 403-429, July.
  3. Andrew McDavid & Lucas Dennis & Patrick Danaher & Greg Finak & Michael Krouse & Alice Wang & Philippa Webster & Joseph Beechem & Raphael Gottardo, 2014. "Modeling Bi-modality Improves Characterization of Cell Cycle on Gene Expression in Single Cells," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-10, July.
  4. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  5. Yli-Heikkilä, Maria & Tauriainen, Jukka, 2014. "Profitability prediction model for dairy farms using the random forest method," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182846, European Association of Agricultural Economists.
  6. Wenjia Wang & Yi-Hui Zhou, 2022. "A Double Penalty Model for Ensemble Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
  7. Efstathios D Gennatas & Ashley Wu & Steve E Braunstein & Olivier Morin & William C Chen & Stephen T Magill & Chetna Gopinath & Javier E Villaneueva-Meyer & Arie Perry & Michael W McDermott & Timothy D, 2018. "Preoperative and postoperative prediction of long-term meningioma outcomes," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-16, September.
  8. Matthew Pietrosanu & Jueyu Gao & Linglong Kong & Bei Jiang & Di Niu, 2021. "Advanced algorithms for penalized quantile and composite quantile regression," Computational Statistics, Springer, vol. 36(1), pages 333-346, March.
  9. Sierra A. Bainter & Thomas G. McCauley & Mahmoud M. Fahmy & Zachary T. Goodman & Lauren B. Kupis & J. Sunil Rao, 2023. "Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1032-1055, September.
  10. Seán Schmitz & Sophia Becker & Laura Weiand & Norman Niehoff & Frank Schwartzbach & Erika von Schneidemesser, 2019. "Determinants of Public Acceptance for Traffic-Reducing Policies to Improve Urban Air Quality," Sustainability, MDPI, vol. 11(14), pages 1-16, July.
  11. Griveau-Billion, Théophile & Richard, Jean-Charles & Roncalli, Thierry, 2013. "A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios," MPRA Paper 49822, University Library of Munich, Germany.
  12. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
  13. Gareth Harman & Dakota Kliamovich & Angelica M Morales & Sydney Gilbert & Deanna M Barch & Michael A Mooney & Sarah W Feldstein Ewing & Damien A Fair & Bonnie J Nagel, 2021. "Prediction of suicidal ideation and attempt in 9 and 10 year-old children using transdiagnostic risk features," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-14, May.
  14. Li, Xinjue & Zboňáková, Lenka & Wang, Weining & Härdle, Wolfgang Karl, 2019. "Combining Penalization and Adaption in High Dimension with Application in Bond Risk Premia Forecasting," IRTG 1792 Discussion Papers 2019-030, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  15. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
  16. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
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  18. Persson, Emma & Häggström, Jenny & Waernbaum, Ingeborg & de Luna, Xavier, 2017. "Data-driven algorithms for dimension reduction in causal inference," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 280-292.
  19. Faisal Zahid & Gerhard Tutz, 2013. "Multinomial logit models with implicit variable selection," 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. 7(4), pages 393-416, December.
  20. Bienvenue Kouwaye & Fabrice Rossi & Noël Fonton & André Garcia & Simplice Dossou-Gbété & Mahouton Norbert Hounkonnou & Gilles Cottrell, 2017. "Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-14, October.
  21. Zakariya Yahya Algamal & Muhammad Hisyam Lee, 2019. "A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification," 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(3), pages 753-771, September.
  22. Guo, Yanhong & Li, Ping & Li, Aihua, 2021. "Tail risk contagion between international financial markets during COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 73(C).
  23. Tarcila Neves Generoso & Demetrius David Silva & Ricardo Santos Silva Amorim & Lineu Neiva Rodrigues & Erli Pinto Santos, 2022. "Methodology for Estimating Streamflow by Water Balance and Rating Curve Methods Based on Logistic Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4389-4402, September.
  24. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
  25. Fogliato Riccardo & Oliveira Natalia L. & Yurko Ronald, 2021. "TRAP: a predictive framework for the Assessment of Performance in Trail Running," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 129-143, June.
  26. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
  27. Breinlich, Holger & Corradi, Valentina & Rocha, Nadia & Ruta, Michele & Silva, J.M.C. Santos & Zylkin, Tom, 2021. "Machine learning in international trade research - evaluating the impact of trade agreements," LSE Research Online Documents on Economics 114379, London School of Economics and Political Science, LSE Library.
  28. Sean M Devlin & Axel Martin & Irina Ostrovnaya, 2021. "Identifying prognostic pairwise relationships among bacterial species in microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-12, November.
  29. Sarah Perrin & Thierry Roncalli, 2019. "Machine Learning Optimization Algorithms & Portfolio Allocation," Papers 1909.10233, arXiv.org.
  30. Heewon Park & Teppei Shimamura & Satoru Miyano & Seiya Imoto, 2014. "Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
  31. Jason T. Kerwin & Rebecca L. Thornton, 2021. "Making the Grade: The Sensitivity of Education Program Effectiveness to Input Choices and Outcome Measures," The Review of Economics and Statistics, MIT Press, vol. 103(2), pages 251-264, May.
  32. Miyazaki, Izuru, 2023. "Recovery of partly sparse and dense signals," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
  33. 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.
  34. Narajewski, Michał & Ziel, Florian, 2020. "Econometric modelling and forecasting of intraday electricity prices," Journal of Commodity Markets, Elsevier, vol. 19(C).
  35. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
  36. Lubomír Štěpánek & Jana Dlouhá & Patrícia Martinková, 2023. "Item Difficulty Prediction Using Item Text Features: Comparison of Predictive Performance across Machine-Learning Algorithms," Mathematics, MDPI, vol. 11(19), pages 1-30, September.
  37. Jie Xiong & Zhitong Bing & Yanlin Su & Defeng Deng & Xiaoning Peng, 2014. "An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
  38. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
  39. Jie Yang & Qianghu Wang & Ze-Yan Zhang & Lihong Long & Ravesanker Ezhilarasan & Jerome M. Karp & Aristotelis Tsirigos & Matija Snuderl & Benedikt Wiestler & Wolfgang Wick & Yinsen Miao & Jason T. Huse, 2022. "DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  40. Fan, Jianqing & Ke, Yuan & Wang, Kaizheng, 2020. "Factor-adjusted regularized model selection," Journal of Econometrics, Elsevier, vol. 216(1), pages 71-85.
  41. repec:jss:jstsof:47:i09 is not listed on IDEAS
  42. Laura Lewis & Hsin-Yuan Huang & Viet T. Tran & Sebastian Lehner & Richard Kueng & John Preskill, 2024. "Improved machine learning algorithm for predicting ground state properties," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
  43. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2023. "Granger Causality Testing in High-Dimensional VARs: A Post-Double-Selection Procedure," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 915-958.
  44. Brieuc Lehmann & Maxine Mackintosh & Gil McVean & Chris Holmes, 2023. "Optimal strategies for learning multi-ancestry polygenic scores vary across traits," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  45. Phillips, Nathaniel D. & Neth, Hansjörg & Woike, Jan K. & Gaissmaier, Wolfgang, 2017. "FFTrees: A toolbox to create, visualize, and evaluate fast-and-frugal decision trees," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(4), pages 344-368.
  46. Dunkler, Daniela & Sauerbrei, Willi & Heinze, Georg, 2016. "Global, Parameterwise and Joint Shrinkage Factor Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i08).
  47. Rochford, L.M. & Bulovic, N. & Ordens, C.M. & McIntyre, N., 2023. "What makes them pump? Factors influencing groundwater extraction for cattle grazing in a semi-arid region," Agricultural Water Management, Elsevier, vol. 279(C).
  48. Yoonsuh Jung, 2018. "Multiple predicting K-fold cross-validation for model selection," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 197-215, January.
  49. Wang, Siyang & Cui, Hengjian, 2015. "A new test for part of high dimensional regression coefficients," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 187-203.
  50. Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Forecasting Stock Market Returns in Advanced Economies over a Century," Mathematics, MDPI, vol. 11(9), pages 1-21, April.
  51. Florian Rohart & Benoît Gautier & Amrit Singh & Kim-Anh Lê Cao, 2017. "mixOmics: An R package for ‘omics feature selection and multiple data integration," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-19, November.
  52. Shuai Luo & Hongyue Sun & Qingyun Ping & Ran Jin & Zhen He, 2016. "A Review of Modeling Bioelectrochemical Systems: Engineering and Statistical Aspects," Energies, MDPI, vol. 9(2), pages 1-27, February.
  53. Murat Genç & M. Revan Özkale, 2021. "Usage of the GO estimator in high dimensional linear models," Computational Statistics, Springer, vol. 36(1), pages 217-239, March.
  54. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
  55. Sill, Martin & Hielscher, Thomas & Becker, Natalia & Zucknick, Manuela, 2014. "c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i05).
  56. Biehl, Alec & Chen, Ying & Sanabria-Véaz, Karla & Uttal, David & Stathopoulos, Amanda, 2019. "Where does active travel fit within local community narratives of mobility space and place?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 123(C), pages 269-287.
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  58. Achim Ahrens & Arnab Bhattacharjee, 2015. "Two-Step Lasso Estimation of the Spatial Weights Matrix," Econometrics, MDPI, vol. 3(1), pages 1-28, March.
  59. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
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