Regularization and variable selection via the elastic net
Trevor Hastie, Department of Statistics, Stanford University, Stanford, CA 94305, USA.
E‐mail: hastie@stanford.edu
Abstract
Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the p≫n case. An algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lasso.
Citing Literature
Number of times cited according to CrossRef: 6063
- Olga Kosheleva, Vladik Kreinovich, Beyond p-Boxes and Interval-Valued Moments: Natural Next Approximations to General Imprecise Probabilities, Statistical and Fuzzy Approaches to Data Processing, with Applications to Econometrics and Other Areas, 10.1007/978-3-030-45619-1_11, (133-143), (2021).
- Chalerm Jaitang, Paravee Maneejuk, Pitchaya Boonsrirat, Sustainable Entrepreneurship on Thailand’s SMEs, Behavioral Predictive Modeling in Economics, 10.1007/978-3-030-49728-6_28, (423-436), (2021).
- Tianyu Zhang, Liwei Zhang, Philip R. O. Payne, Fuhai Li, Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models, Translational Bioinformatics for Therapeutic Development, 10.1007/978-1-0716-0849-4_12, (223-238), (2021).
- Igor Isaev, Olga Sarmanova, Sergey Burikov, Tatiana Dolenko, Kirill Laptinskiy, Sergey Dolenko, Feature Selection in Neural Network Solution of Inverse Problem Based on Integration of Optical Spectroscopic Methods, Advances in Neural Computation, Machine Learning, and Cognitive Research IV, 10.1007/978-3-030-60577-3_27, (234-241), (2021).
- Ai Ni, Chi Song, Variable Selection for Time-to-Event Data, Translational Bioinformatics for Therapeutic Development, 10.1007/978-1-0716-0849-4_5, (61-76), (2021).
- Marco C. Pinho, Kaustav Bera, Niha Beig, Pallavi Tiwari, MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses, Brain Tumors, 10.1007/978-1-0716-0856-2_14, (323-368), (2021).
- Vicente Gallego, Ana Sánchez, Isabel Martón, Sebastián Martorell, Analysis of occupational accidents in Spain using shrinkage regression methods, Safety Science, 10.1016/j.ssci.2020.105000, 133, (105000), (2021).
- Mainak Sarkar, Arnaud De Bruyn, LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning, Journal of Interactive Marketing, 10.1016/j.intmar.2020.07.002, 53, (80-95), (2021).
- Michael S. Watt, David J. Palmer, Ellen Mae C. Leonardo, Maxime Bombrun, Use of advanced modelling methods to estimate radiata pine productivity indices, Forest Ecology and Management, 10.1016/j.foreco.2020.118557, 479, (118557), (2021).
- Bin Shi, S. S. Iyengar, Bin Shi, S. S. Iyengar, Related Work on Geometry of Non-Convex Programs, Mathematical Theories of Machine Learning - Theory and Applications, 10.1007/978-3-030-17076-9, (39-44), (2020).
- Bin Shi, S. S. Iyengar, Bin Shi, S. S. Iyengar, Development of Novel Techniques of CoCoSSC Method, Mathematical Theories of Machine Learning - Theory and Applications, 10.1007/978-3-030-17076-9, (29-33), (2020).
- Bin Shi, S. S. Iyengar, Bin Shi, S. S. Iyengar, Online Discovery for Stable and Grouping Causalities in Multivariate Time Series, Mathematical Theories of Machine Learning - Theory and Applications, 10.1007/978-3-030-17076-9, (103-119), (2020).
- M. Noori Asl, H. Bevrani, R. Arabi Belaghi, Syed Ejaz Ahmed, Shrinkage and Sparse Estimation for High-Dimensional Linear Models, Proceedings of the Thirteenth International Conference on Management Science and Engineering Management, 10.1007/978-3-030-21248-3_11, (147-156), (2020).
- Luca Oneto, Luca Oneto, The “Five W” of MS and EE, Model Selection and Error Estimation in a Nutshell, 10.1007/978-3-030-24359-3_2, (5-11), (2020).
- Davide Benedetti, Rastislav Molnar, Stress Testing Corporate Earnings of US Companies, Data-Centric Business and Applications, 10.1007/978-3-030-19069-9_14, (347-370), (2020).
- Bin Liu, Hongrun Yang, Huanwen Lv, Lan Li, Futing Jing, Jiajia Liu, Sheng Wang, Neutron spectrum unfolding of the multiple activation foils based on sparse representation, Annals of Nuclear Energy, 10.1016/j.anucene.2019.106947, 135, (106947), (2020).
- Sarah Warren, Patrick Danaher, Afshin Mashadi-Hossein, Lynell Skewis, Brett Wallden, Sean Ferree, Alessandra Cesano, Development of Gene Expression-Based Biomarkers on the nCounter® Platform for Immuno-Oncology Applications, Biomarkers for Immunotherapy of Cancer, 10.1007/978-1-4939-9773-2_13, (273-300), (2020).
- Juntao Li, Mingming Chang, Pengjie Tian, Liuyuan Chen, Xiaoxia Mu, Personal Credit Scoring via Logistic Regression with Elastic Net Penalty, Proceedings of 2019 Chinese Intelligent Systems Conference, 10.1007/978-981-32-9682-4_44, (422-428), (2020).
- Nedret Billor, Asuman S. Turkmen, Emergence of Statistical Methodologies with the Rise of BIG Data, Women in Industrial and Systems Engineering, 10.1007/978-3-030-11866-2_2, (27-48), (2020).
- Mohit Goyal, Rajan Goyal, P. Venkatappa Reddy, Brejesh Lall, Activation Functions, Deep Learning: Algorithms and Applications, 10.1007/978-3-030-31760-7_1, (1-30), (2020).
- Ender Konukoglu, Ben Glocker, Random forests in medical image computing, Handbook of Medical Image Computing and Computer Assisted Intervention, 10.1016/B978-0-12-816176-0.00024-7, (457-480), (2020).
- Yu-Chen Lo, Gui Ren, Hiroshi Honda, Kara L. Davis, Artificial Intelligence-Based Drug Design and Discovery, Cheminformatics and its Applications [Working Title], 10.5772/intechopen.83236, (2020).
- Vasileios C. Pezoulas, Themis P. Exarchos, Konstantina D. Kourou, Athanasios G. Tzioufas, Salvatore De Vita, Dimitrios I. Fotiadis, Utilizing Incremental Learning for the Prediction of Disease Outcomes Across Distributed Clinical Data: A Framework and a Case Study, XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019, 10.1007/978-3-030-31635-8_98, (823-831), (2020).
- Robert Whelan, Zhipeng Cao, Laura O'Halloran, Brian Pennie, Genetics, imaging, and cognition, Cognition and Addiction, 10.1016/B978-0-12-815298-0.00027-7, (365-377), (2020).
- Fezai R., Mansouri M., Abodayeh K., Nounou H., Nounou M., Online reduced gaussian process regression based generalized likelihood ratio test for fault detection, Journal of Process Control, 10.1016/j.jprocont.2019.11.002, 85, (30-40), (2020).
- Alexander Smith, Andrea Keane, James A. Dumesic, George W. Huber, Victor M. Zavala, A machine learning framework for the analysis and prediction of catalytic activity from experimental data, Applied Catalysis B: Environmental, 10.1016/j.apcatb.2019.118257, 263, (118257), (2020).
- Han Cao, Emanuel Schwarz, Opportunities and challenges of machine learning approaches for biomarker signature identification in psychiatry, Personalized Psychiatry, 10.1016/B978-0-12-813176-3.00011-0, (117-126), (2020).
- Scott R. Clark, Micah Cearns, Klaus Oliver Schubert, Bernhard T. Baune, Multimodal modeling for personalized psychiatry, Personalized Psychiatry, 10.1016/B978-0-12-813176-3.00043-2, (521-536), (2020).
- Gorkem Bostanci, Kamil Yilmaz, How Connected is the Global Sovereign Credit Risk Network?, Journal of Banking & Finance, 10.1016/j.jbankfin.2020.105761, (105761), (2020).
- Kai Wang, Junghui Chen, Zhihuan Song, A sparse loading-based contribution method for multivariate control performance diagnosis, Journal of Process Control, 10.1016/j.jprocont.2019.12.001, 85, (199-213), (2020).
- Michael T. Gorczyca, Kifle G. Gebremedhin, Ranking of environmental heat stressors for dairy cows using machine learning algorithms, Computers and Electronics in Agriculture, 10.1016/j.compag.2019.105124, 168, (105124), (2020).
- Sandra Vieira, Walter Hugo Lopez Pinaya, Andrea Mechelli, Main concepts in machine learning, Machine Learning, 10.1016/B978-0-12-815739-8.00002-X, (21-44), (2020).
- J. Camacho, A.K. Smilde, E. Saccenti, J.A. Westerhuis, All sparse PCA models are wrong, but some are useful. Part I: Computation of scores, residuals and explained variance, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2019.103907, 196, (103907), (2020).
- Guang-Hui Fu, Yuan-Jiao Wu, Min-Jie Zong, Lun-Zhao Yi, Feature selection and classification by minimizing overlap degree for class-imbalanced data in metabolomics, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2019.103906, 196, (103906), (2020).
- Andre F. Marquand, Seyed Mostafa Kia, Linear methods for classification, Machine Learning, 10.1016/B978-0-12-815739-8.00005-5, (83-100), (2020).
- Fan Zhang, Lauren J. O'Donnell, Support vector regression, Machine Learning, 10.1016/B978-0-12-815739-8.00007-9, (123-140), (2020).
- M.A. González-Rodríguez, U. Diéguez-Aranda, Exploring the use of learning techniques for relating the site index of radiata pine stands with climate, soil and physiography, Forest Ecology and Management, 10.1016/j.foreco.2019.117803, 458, (117803), (2020).
- Adrian Fisher, John Armston, Nicholas Goodwin, Peter Scarth, Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands, Remote Sensing of Environment, 10.1016/j.rse.2019.111520, 237, (111520), (2020).
- Huiyan Luo, Qi Zhao, Wei Wei, Lianghong Zheng, Shaohua Yi, Gen Li, Wenqiu Wang, Hui Sheng, Hengying Pu, Haiyu Mo, Zhixiang Zuo, Zexian Liu, Chaofeng Li, Chuanbo Xie, Zhaolei Zeng, Weimin Li, Xiaoke Hao, Yuying Liu, Sumei Cao, Wanli Liu, Sarah Gibson, Kang Zhang, Guoliang Xu, Rui-hua Xu, Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer, Science Translational Medicine, 10.1126/scitranslmed.aax7533, 12, 524, (eaax7533), (2020).
- Bahadır Yüzbaşı, Mohammad Arashi, S. Ejaz Ahmed, Shrinkage Estimation Strategies in Generalised Ridge Regression Models: Low/High‐Dimension Regime, International Statistical Review, 10.1111/insr.12351, 88, 1, (229-251), (2020).
- Liyuan Gao, Yongmei Ding, Linxin Zhang, High dimensional regression coefficient compression model and its application, Journal of Physics: Conference Series, 10.1088/1742-6596/1437/1/012119, 1437, (012119), (2020).
- Michael A. Kiebish, Jennifer Cullen, Prachi Mishra, Amina Ali, Eric Milliman, Leonardo O. Rodrigues, Emily Y. Chen, Vladimir Tolstikov, Lixia Zhang, Kiki Panagopoulos, Punit Shah, Yongmei Chen, Gyorgy Petrovics, Inger L. Rosner, Isabell A. Sesterhenn, David G. McLeod, Elder Granger, Rangaprasad Sarangarajan, Viatcheslav Akmaev, Alagarsamy Srinivasan, Shiv Srivastava, Niven R. Narain, Albert Dobi, Multi-omic serum biomarkers for prognosis of disease progression in prostate cancer, Journal of Translational Medicine, 10.1186/s12967-019-02185-y, 18, 1, (2020).
- Kai Carstensen, Markus Heinrich, Magnus Reif, Maik H. Wolters, Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model, International Journal of Forecasting, 10.1016/j.ijforecast.2019.09.005, (2020).
- Thomas Breen, Benjamin Brueske, Mandeep S. Sidhu, Dennis H. Murphree, Kianoush B. Kashani, Gregory W. Barsness, Jacob C. Jentzer, Abnormal Serum Sodium is Associated With Increased Mortality Among Unselected Cardiac Intensive Care Unit Patients, Journal of the American Heart Association, 10.1161/JAHA.119.014140, 9, 2, (2020).
- Attila Csala, Aeilko H. Zwinderman, Michel H. Hof, Multiset sparse partial least squares path modeling for high dimensional omics data analysis, BMC Bioinformatics, 10.1186/s12859-019-3286-3, 21, 1, (2020).
- Tamas Spisak, Balint Kincses, Frederik Schlitt, Matthias Zunhammer, Tobias Schmidt-Wilcke, Zsigmond T. Kincses, Ulrike Bingel, Pain-free resting-state functional brain connectivity predicts individual pain sensitivity, Nature Communications, 10.1038/s41467-019-13785-z, 11, 1, (2020).
- Xianfeng Hao, Yuyang Zhao, Yudong Wang, Forecasting the real prices of crude oil using robust regression models with regularization constraints, Energy Economics, 10.1016/j.eneco.2020.104683, (104683), (2020).
- Luca Zanin, Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market, Journal of Behavioral and Experimental Finance, 10.1016/j.jbef.2020.100272, (100272), (2020).
- Hongwei Zhang, Qiang He, Ben Jacobsen, Fuwei Jiang, Forecasting stock returns with model uncertainty and parameter instability, Journal of Applied Econometrics, 10.1002/jae.2747, 35, 5, (629-644), (2020).
- Harsha Vaddireddy, Adil Rasheed, Anne E. Staples, Omer San, Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data, Physics of Fluids, 10.1063/1.5136351, 32, 1, (015113), (2020).
- Hossein S. Aghamiry, Ali Gholami, Stephane Operto, Compound Regularization of Full-Waveform Inversion for Imaging Piecewise Media, IEEE Transactions on Geoscience and Remote Sensing, 10.1109/TGRS.2019.2944464, 58, 2, (1192-1204), (2020).
- Song Yan, Johan Wirta, Joni-Kristian Kämäräinen, Anthropometric clothing measurements from 3D body scans, Machine Vision and Applications, 10.1007/s00138-019-01054-4, 31, 1, (2020).
- Yuriko Okazaki, Shinichiro Okazaki, Shingo Asamoto, Pang‐jo Chun, Applicability of machine learning to a crack model in concrete bridges, Computer-Aided Civil and Infrastructure Engineering, 10.1111/mice.12532, 35, 8, (775-792), (2020).
- Hanni Kiiski, Marc Bennett, Laura M. Rueda‐Delgado, Francesca R. Farina, Rachel Knight, Rory Boyle, Darren Roddy, Katie Grogan, Jessica Bramham, Clare Kelly, Robert Whelan, EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD, European Journal of Neuroscience, 10.1111/ejn.14645, 51, 10, (2095-2109), (2020).
- Shengen Hu, Dawei Huo, Zhaowei Yu, Yujie Chen, Jing Liu, Lin Liu, Xudong Wu, Yong Zhang, ncHMR detector: a computational framework to systematically reveal non-classical functions of histone modification regulators, Genome Biology, 10.1186/s13059-020-01953-0, 21, 1, (2020).
- Vicky Evangelidis‐Sakellson, Carol Kunzel, Sunmoo Yoon, Impact of Dental Students’ Faculty Group Leader, Intended Postgraduate Training, and Clinic Schedule on Their Clinical Performance: A Retrospective Study at a U.S. Dental School, Journal of Dental Education, 10.21815/JDE.019.165, 84, 1, (34-43), (2020).
- Emanuela Trenti, Stefan Pycha, Christine Mian, Christine Schwienbacher, Esther Hanspeter, Mona Kafka, Giorgio Alfredo Spedicato, Egils Vjaters, Stephan Degener, Armin Pycha, Carolina D’Elia, Comparison of 2 new real‐time polymerase chain reaction–based urinary markers in the follow‐up of patients with non–muscle‐invasive bladder cancer, Cancer Cytopathology, 10.1002/cncy.22246, 128, 5, (341-347), (2020).
- Paola Galdi, Manuel Blesa, David Q. Stoye, Gemma Sullivan, Gillian J. Lamb, Alan J. Quigley, Michael J. Thrippleton, Mark E. Bastin, James P. Boardman, Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth, NeuroImage: Clinical, 10.1016/j.nicl.2020.102195, (102195), (2020).
- Chaehyeon Lee, Sajan Maharjan, Kyungchan Ko, James Won-Ki Hong, Toward Detecting Illegal Transactions on Bitcoin Using Machine-Learning Methods, Blockchain and Trustworthy Systems, 10.1007/978-981-15-2777-7_42, (520-533), (2020).
- Takuya Oishi, Yoshihiro Hayashi, Miho Noguchi, Fumiaki Yano, Shungo Kumada, Kozo Takayama, Kotaro Okada, Yoshinori Onuki, Creation of novel large dataset comprising several granulation methods and the prediction of tablet properties from critical material attributes and critical process parameters using regularized linear regression models including interaction terms, International Journal of Pharmaceutics, 10.1016/j.ijpharm.2020.119083, (119083), (2020).
- Yinsen Miao, Jeong Hwan Kook, Yadong Lu, Michele Guindani, Marina Vannucci, Scalable Bayesian variable selection regression models for count data, Flexible Bayesian Regression Modelling, 10.1016/B978-0-12-815862-3.00015-9, (187-219), (2020).
- Siddhartha Mandal, Kishore K. Madhipatla, Sarath Guttikunda, Itai Kloog, Dorairaj Prabhakaran, Joel D. Schwartz, Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016, Atmospheric Environment, 10.1016/j.atmosenv.2020.117309, (117309), (2020).
- Atif Riaz, Muhammad Asad, Eduardo Alonso, Greg Slabaugh, DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI, Journal of Neuroscience Methods, 10.1016/j.jneumeth.2019.108506, (108506), (2020).
- Pere L. Gilabert, David López‐Bueno, Thi Quynh Anh Pham, Gabriel Montoro, Machine Learning for Digital Front‐End, Machine Learning for Future Wireless Communications, 10.1002/9781119562306, (327-381), (2020).
- Reza Rasti, Michael J. Allingham, Priyatham S. Mettu, Sam Kavusi, Kishan Govind, Scott W. Cousins, Sina Farsiu, Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema, Biomedical Optics Express, 10.1364/BOE.379150, 11, 2, (1139), (2020).
- S. A. Rahman, R. C. Walker, M. A. Lloyd, B. L. Grace, G. I. van Boxel, B. F. Kingma, J. P. Ruurda, R. van Hillegersberg, S. Harris, S. Parsons, S. Mercer, E. A. Griffiths, J. R. O'Neill, R. Turkington, R. C. Fitzgerald, T. J. Underwood, Ayesha Noorani, Rachael Fels Elliott, Paul A.W. Edwards, Nicola Grehan, Barbara Nutzinger, Jason Crawte, Hamza Chettouh, Gianmarco Contino, Xiaodun Li, Eleanor Gregson, Sebastian Zeki, Rachel de la Rue, Shalini Malhotra, Simon Tavaré, Andy G. Lynch, Mike L. Smith, Jim Davies, Charles Crichton, Nick Carroll, Peter Safranek, Andrew Hindmarsh, Vijayendran Sujendran, Stephen J. Hayes, Yeng Ang, Shaun R. Preston, Sarah Oakes, Izhar Bagwan, Vicki Save, Richard J.E. Skipworth, Ted R. Hupp, J. Robert O'Neill, Olga Tucker, Andrew Beggs, Philippe Taniere, Sonia Puig, Timothy J. Underwood, Fergus Noble, James P. Byrne, Jamie J. Kelly, Jack Owsley, Hugh Barr, Neil Shepherd, Oliver Old, Jesper Lagergren, James Gossage, Andrew Davies Fuju Chang, Janine Zylstra, Vicky Goh, Francesca D. Ciccarelli, Grant Sanders, Richard Berrisford, Catherine Harden, David Bunting, Mike Lewis, Ed Cheong, Bhaskar Kumar, Simon L. Parsons, Irshad Soomro, Philip Kaye, John Saunders, Laurence Lovat, Rehan Haidry, Victor Eneh, Laszlo Igali, Michael Scott, Shamila Sothi, Sari Suortamo, Suzy Lishman, George B. Hanna, Christopher J. Peters, Anna Grabowska, Machine learning to predict early recurrence after oesophageal cancer surgery, BJS (British Journal of Surgery), 10.1002/bjs.11461, 107, 8, (1042-1052), (2020).
- Aven Satre-Meloy, Marina Diakonova, Philipp Grünewald, Cluster analysis and prediction of residential peak demand profiles using occupant activity data, Applied Energy, 10.1016/j.apenergy.2019.114246, 260, (114246), (2020).
- Ernesto Carrella, Steven Saul, Kristin Marshall, Matthew G. Burgess, Reniel B. Cabral, Richard M. Bailey, Chris Dorsett, Michael Drexler, Jens Koed Madsen, Andreas Merkl, Simple Adaptive Rules Describe Fishing Behaviour Better than Perfect Rationality in the US West Coast Groundfish Fishery, Ecological Economics, 10.1016/j.ecolecon.2019.106449, 169, (106449), (2020).
- Youssef Oulhote, David C. Bellinger, Neurodevelopment and Neurobehavioral Disorders in Relation to Developmental Exposures, Health Impacts of Developmental Exposure to Environmental Chemicals, 10.1007/978-981-15-0520-1_7, (153-174), (2020).
- M. Aucejo, O. De Smet, Multi-parameter multiplicative regularization: An application to force reconstruction problems, Journal of Sound and Vibration, 10.1016/j.jsv.2019.115135, 469, (115135), (2020).
- Junjie Chen, Conor Nodzak, Statistical and Machine Learning Methods for eQTL Analysis, eQTL Analysis, 10.1007/978-1-0716-0026-9_7, (87-104), (2020).
- Adil Rasheed, Omer San, Trond Kvamsdal, Digital Twin: Values, Challenges and Enablers From a Modeling Perspective, IEEE Access, 10.1109/ACCESS.2020.2970143, 8, (21980-22012), (2020).
- Xiaolong Chen, Zhizhong Mao, Runda Jia, A new multiple kernel-based regularization method for identification of delay linear dynamic systems, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2020.103971, (103971), (2020).
- K.C. Giglia, C. Aldrich, Operational state detection in hydrocyclones with convolutional neural networks and transfer learning, Minerals Engineering, 10.1016/j.mineng.2020.106211, 149, (106211), (2020).
- Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar, Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks, IEEE Journal of Biomedical and Health Informatics, 10.1109/JBHI.2019.2929264, 24, 2, (424-436), (2020).
- Peter Soroye, Tim Newbold, Jeremy Kerr, Climate change contributes to widespread declines among bumble bees across continents, Science, 10.1126/science.aax8591, 367, 6478, (685-688), (2020).
- Mathilde Couëtoux du Tertre, Maud Marques, Suzan McNamara, Karen Gambaro, Cyrla Hoffert, Lise Tremblay, Nicole Bouchard, Razvan Diaconescu, Normand Blais, Christian Couture, Vincent Pelsser, Hangjun Wang, Laura McIntosh, Valérie Hindie, Stephane Parent, Laetitia Cortes, Yannick-André Breton, Gwenael Pottiez, Pascal Croteau, Valerie Higenell, Luisa Izzi, Alan Spatz, Victor Cohen, Gerald Batist, Jason Agulnik, Discovery of a putative blood-based protein signature associated with response to ALK tyrosine kinase inhibition, Clinical Proteomics, 10.1186/s12014-020-9269-6, 17, 1, (2020).
- Jianqing Fan, Yuan Ke, Kaizheng Wang, Factor-adjusted regularized model selection, Journal of Econometrics, 10.1016/j.jeconom.2020.01.006, (2020).
- Ahmad Mani-Varnosfaderani, Mohammad Javad Masroor, Yadollah Yamini, Evaluating different sparsity measures for resolving LC/GC-MS data in the context of multivariate curve resolution, Chemometrics and Intelligent Laboratory Systems, 10.1016/j.chemolab.2020.104004, 200, (104004), (2020).
- Sondes Gharsellaoui, Majdi Mansouri, Mohamed Trabelsi, Shady S. Refaat, Hassani Messaoud, Fault diagnosis of heating systems using multivariate feature extraction based machine learning classifiers, Journal of Building Engineering, 10.1016/j.jobe.2020.101221, (101221), (2020).
- Hasan Bulut, The construction of a composite index for general satisfaction in Turkey and the investigation of its determinants, Socio-Economic Planning Sciences, 10.1016/j.seps.2020.100811, (100811), (2020).
- Yinnan Zheng, Cuilin Zhang, Marc G. Weisskopf, Paige L. Williams, Birgit Claus Henn, Patrick J. Parsons, Christopher D. Palmer, Germaine M. Buck Louis, Tamarra James-Todd, Evaluating associations between early pregnancy trace elements mixture and 2nd trimester gestational glucose levels: A comparison of three statistical approaches, International Journal of Hygiene and Environmental Health, 10.1016/j.ijheh.2019.113446, 224, (113446), (2020).
- Jon D. Elhai, Haibo Yang, Dmitri Rozgonjuk, Christian Montag, Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out, Addictive Behaviors, 10.1016/j.addbeh.2019.106261, 103, (106261), (2020).
- Hyungrok Do, Myun-Seok Cheon, Seoung Bum Kim, Graph Structured Sparse Subset Selection, Information Sciences, 10.1016/j.ins.2019.12.086, (2020).
- Xuejun Wang, Feilong Cao, Wenjian Wang, Adaptive sparse and dense hybrid representation with nonconvex optimization, Frontiers of Computer Science, 10.1007/s11704-019-7200-y, 14, 4, (2020).
- Parham Hashemzadeh, A. S. Fokas, C. B. Schönlieb, A hybrid analytical–numerical algorithm for determining the neuronal current via electroencephalography, Journal of The Royal Society Interface, 10.1098/rsif.2019.0831, 17, 163, (20190831), (2020).
- Quoc Thong Nguyen, Rémy Fouchereau, Emmanuel Frénod, Christine Gerard, Vincent Sincholle, Comparison of forecast models of production of dairy cows combining animal and diet parameters, Computers and Electronics in Agriculture, 10.1016/j.compag.2020.105258, 170, (105258), (2020).
- Dan Hu, Zhengwang Wu, Weili Lin, Gang Li, Dinggang Shen, Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features, IEEE Journal of Biomedical and Health Informatics, 10.1109/JBHI.2019.2897020, 24, 1, (214-225), (2020).
- Usama Pervaiz, Diego Vidaurre, Mark W. Woolrich, Stephen M. Smith, Optimising network modelling methods for fMRI, NeuroImage, 10.1016/j.neuroimage.2020.116604, (116604), (2020).
- Masashi Kitazawa, Tomohisa Hatta, Yusuke Sasaki, Kazuhiko Fukui, Koji Ogawa, Eriko Fukuda, Naoki Goshima, Natsuko Okita, Yasuhide Yamada, Hitoshi Nakagama, Tohru Natsume, Katsuhisa Horimoto, Promotion of the Warburg effect is associated with poor benefit from adjuvant chemotherapy in colorectal cancer, Cancer Science, 10.1111/cas.14275, 111, 2, (658-666), (2020).
- Brian D. Gerber, Joseph M. Northrup, Improving spatial predictions of animal resource selection to guide conservation decision making, Ecology, 10.1002/ecy.2953, 101, 3, (2020).
- Ronak Etemadpour, Yongcheng Zhu, Qizhi Zhao, Yilun Hu, Bohan Chen, Mohammed Asif Sharier, Shirong Zheng, Jose Gustavo S. Paiva, Role of absence in academic success: an analysis using visualization tools, Smart Learning Environments, 10.1186/s40561-019-0112-3, 7, 1, (2020).
- L. Alfredo Fernandez-Jimenez, Sonia Terreros-Olarte, Pedro J. Zorzano-Santamaria, Montserrat Mendoza-Villena, Eduardo Garcia-Garrido, Probabilistic photovoltaic power forecasting model based on deterministic forecasts, E3S Web of Conferences, 10.1051/e3sconf/202015201003, 152, (01003), (2020).
- Yan Li, Dayou Liu, Tengfei Li, Yungang Zhu, Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations, BMC Bioinformatics, 10.1186/s12859-019-3314-3, 21, 1, (2020).
- Peiran Jiang, Shujun Huang, Zhenyuan Fu, Zexuan Sun, Ted M. Lakowski, Pingzhao Hu, Deep graph embedding for prioritizing synergistic anticancer drug combinations, Computational and Structural Biotechnology Journal, 10.1016/j.csbj.2020.02.006, (2020).
- Jiaji Zhu, Waqas Ahmad, Tianhui Jiao, Jing Xu, Jingjing Wang, Quansheng Chen, Hui Jiang, Huanhuan Li, Interval combination iterative optimization approach coupled with SIMPLS (ICIOA-SIMPLS) for quantitative analysis of surface-enhanced Raman scattering (SERS) spectra, Analytica Chimica Acta, 10.1016/j.aca.2020.01.018, (2020).
- Crispin M. Mutshinda, Andrew J. Irwin, Mikko J. Sillanpää, A Bayesian Framework for Robust Quantitative Trait Locus Mapping and Outlier Detection, The International Journal of Biostatistics, 10.1515/ijb-2019-0038, 0, 0, (2020).
- Dionissios T. Hristopulos, Dionissios T. Hristopulos, Trend Models and Estimation, Random Fields for Spatial Data Modeling, 10.1007/978-94-024-1918-4_2, (41-81), (2020).
- Matthew S Ji, Blaine S C Eldred, Regina Liu, Sean T Pianka, Donna Molaie, Bryan Kevan, Stephanie Pan, Thomas J Lai, Nhung T Nguyen, Frances E Chow, William H Yong, Christopher D Cox, Devin N Reeh, Tie Li, Linda M Liau, Phioanh L Nghiemphu, Timothy F Cloughesy, Gang Li, Albert Lai, Targeted next-generation sequencing of 565 neuro-oncology patients at UCLA: A single-institution experience, Neuro-Oncology Advances, 10.1093/noajnl/vdaa009, 2, 1, (2020).
- Zhi Zhao, Manuela Zucknick, Structured penalized regression for drug sensitivity prediction, Journal of the Royal Statistical Society: Series C (Applied Statistics), 10.1111/rssc.12400, 69, 3, (525-545), (2020).
- See more
