The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
A Sparse-Group Lasso
Abstract
For high-dimensional supervised learning problems, often using problem-specific assumptions can lead to greater accuracy. For problems with grouped covariates, which are believed to have sparse effects both on a group and within group level, we introduce a regularized model for linear regression with ℓ1 and ℓ2 penalties. We discuss the sparsity and other regularization properties of the optimal fit for this model, and show that it has the desired effect of group-wise and within group sparsity. We propose an algorithm to fit the model via accelerated generalized gradient descent, and extend this model and algorithm to convex loss functions. We also demonstrate the efficacy of our model and the efficiency of our algorithm on simulated data. This article has online supplementary material.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Self-citationRegularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Self-citationRegularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Self-citationModel Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
The Group Lasso for Logistic Regression
Meier, L., van de Geer, S., & Bühlmann, P. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00627.x · Cited by 1,681
Ma, X.-J., Wang, Z., Ryan, P. D., Isakoff, S. J., Barmettler, A., Fuller, A. P., Muir, B., Mohapatra, G., Salunga, R. C., Tuggle, J. T., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.05.015 · Cited by 959
NESTA: A Fast and Accurate First-Order Method for Sparse Recovery
Becker, S., Bobin, J., & Candès, E. J. (2009) · Cited by 897
Gradient methods for minimizing composite objective function
Nesterov, Y. E. (2007) · Cited by 867
Group lasso with overlap and graph lasso
Jacob, L., Obozinski, G., & Vert, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 842
Research works printed but not linked: 1
- Puig, A., Wiesel, A., and Hero, A., 2009. "“A Multidimensional Shrinkage-Thresholding Operator, Statistical Signal Processing,”". In: SSP'09, IEEE/SP 15th Workshop on Statistical Signal Processing. 2009. pp. 113–116.
Cited by
Sorted by citations
Items 601–700 of 1,000
Topology identification of dynamic point process networks
Pasha, S. A., & Solo, V. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853621 · Cited by 7
Rodríguez‐Girondo, M., Salo, P. P., Burzykowski, T., Perola, M., Houwing‐Duistermaat, J. J., & Mertens, B. J. A. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1125 · Cited by 7
Yu, H., Jiang, M., Chen, H., Feng, J., Wang, Y., & Lu, Y. (2017) · Optik · doi:10.1016/j.ijleo.2017.04.069 · Cited by 7
Machine learning methods in computational cancer biology
Vidyasagar, M. (2017) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2017.03.007 · Cited by 7
Multinomial classification with class-conditional overlapping sparse feature groups
Li, X., Zhu, D., & Dong, M. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.11.002 · Cited by 7
Statistical Models to Explore the Exposome: From OMICs Profiling to ‘Mechanome’ Characterization
Chadeau‐Hyam, M., & Vermeulen, R. C. H. (2018) · Springer eBooks · doi:10.1007/978-3-319-89321-1_11 · Cited by 8
Logistic and SVM Credit Score Models Based on Lasso Variable Selection
Li, Q. (2019) · Journal of Applied Mathematics and Physics · doi:10.4236/jamp.2019.75076 · Cited by 7
Image-derived models of cell organization changes during differentiation and drug treatments
Ruan, X., Johnson, G. R., Bierschenk, I., Nitschke, R., Boerries, M., Busch, H. S., & Murphy, R. F. (2019) · Molecular Biology of the Cell · doi:10.1091/mbc.e19-02-0080 · Cited by 7
Genetic Variants Detection Based on Weighted Sparse Group Lasso
Che, K., Chen, X., Guo, M., Wang, C., & Liu, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00155 · Cited by 7
A review of Bayesian group selection approaches for linear regression models
Lai, W., & Chen, R. (2020) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1513 · Cited by 7
Structured sparsity modeling for improved multivariate statistical analysis based fault isolation
Chen, W., Zeng, J., Xu, X., Luo, S., & Gao, C. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2020.12.007 · Cited by 7
Towards Compact Broad Learning System by Combined Sparse Regularization
Miao, J., Yang, T., Jin, J., Sun, L., Niu, L., & Shi, Y. (2021) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622021500553 · Cited by 7
Improving stability of prediction models based on correlated omics data by using network approaches
Tissier, R., Houwing‐Duistermaat, J. J., & Rodríguez‐Girondo, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192853 · Cited by 7
Joint estimation of $K$ related regression models with simple $L_1$-norm penaltiesPreprint
Ollier, É., & Viallon, V. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.1594 · Cited by 7
Group SLOPE - adaptive selection of groups of predictors*
Brzyski, D., Gossmann, A., Su, W., & Bogdan, M. (2018) · Figshare · doi:10.6084/m9.figshare.5787522 · Cited by 7
Matroska Feature-Selection Method for Microarray Dataset (Method 2)
Shinmura, S. (2016) · Springer eBooks · doi:10.1007/978-981-10-2164-0_8 · Cited by 6
Su, T., Liu, X., Lei, B., Cao, Z., Di, J., Xu, L., & Cui, X. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2399332 · Cited by 6
Enhancing sparse identification of nonlinear dynamics with Earth-Mover distance and group similarity
Liu, D., & Sopasakis, A. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0214404 · Cited by 6
Yang, K., Painter, T. H., Deems, J. S., & Molotch, N. P. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114693 · Cited by 6
Variable Selection for Prediction in Clinical Research
Huang, Y., Cho, M., Chakraborty, S., & Dey, T. (2025) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.70030 · Cited by 6
Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso
Hu, S., Xiang, X., Huang, X., Lu, Y. Q., Zhang, X., Yao, D., & Valdes-Sosa, P. A. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1559067 · Cited by 7
Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors
Jin, R., & Tan, A. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1864383 · Cited by 6
Liu, B., Zhang, Q., Xue, L., Song, P. X. ‐., & Kang, J. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2142590 · Cited by 6
Proximal Cost-sensitive Sparse Group Online Learning
Chen, Z., Zhan, H., Sheng, V. S., Edwards, A., & Zhang, K. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10021084 · Cited by 6
Zhang, Z., Sun, H., Li, S., He, J., Cao, J., Cui, G., & Wang, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120635 · Cited by 6
Learning High-Dimensional Differential Graphs From Multiattribute Data
TUGNAIT, J. K. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3343553 · Cited by 6
Robust multidimensional reconstruction via group sparsity with Radon operators
Li, J., & Liu, D. (2024) · Geophysics · doi:10.1190/geo2023-0465.1 · Cited by 6
Limited-Supervised Multi-Label Learning with Dependency Noise
Wang, Y., Zhao, Y., Wang, Z., Shan, W., & Wang, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i14.29494 · Cited by 6
Neural lasso: a unifying approach of lasso and neural networks
Curbelo, E., Delgado‐Gómez, D., & Carreras-García, D. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00546-5 · Cited by 6
A fault diagnosis method based on convolutional sparse representation
Ding, Y., Liu, T., & Wu, F. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104940 · Cited by 6
Sparse Probabilistic Synthesis of Quantum Operations
Koczor, B. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.040352 · Cited by 11
MORE interpretable multi-omic regulatory networks to characterise phenotypes
Aguerralde‐Martin, M., Císcar, M. C., Conesa, A., & Tarazona, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf270 · Cited by 6
A link-free sparse group variable selection method for single-index model
Zeng, B., Wen, X. M., & Zhu, L. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1254731 · Cited by 6
A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions
Molstad, A. J., & Rothman, A. J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1999819 · Cited by 6
Modeling Functional Time Series and Mixed-Type Predictors With Partially Functional Autoregressions
Xu, X., Chen, Y., Zhang, G., & Koch, T. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2021.2011299 · Cited by 6
Structure Level Pruning of Efficient Convolutional Neural Networks with Sparse Group LASSO
(2022) · International Journal of Machine Learning and Computing · doi:10.18178/ijmlc.2022.12.5.1111 · Cited by 6
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification
Bao, R., Gu, B., & Huang, H. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557234 · Cited by 6
Multivariate regression modeling in integrative analysis via sparse regularization
Kawano, S., Fukushima, T., Nakagawa, J., & Oshiki, M. (2025) · Japanese Journal of Statistics and Data Science · doi:10.1007/s42081-025-00312-2 · Cited by 6
Bayesian Variable Selection for Multi-response Linear Regression
Chen, W.-P., Wu, Y., & Chen, R.-B. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-13987-6_8 · Cited by 6
Statistical Learning of Neuronal Functional Connectivity
Zhang, C., Chai, Y., Guo, X., Gao, M., Devilbiss, D. M., & Zhang, Z. (2016) · Technometrics · doi:10.1080/00401706.2016.1142904 · Cited by 6
Indoor WLAN localization using group sparsity optimization technique
Khalajmehrabadi, A., Gatsis, N., & Akopian, D. (2016) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans.2016.7479749 · Cited by 6
Sparsity Within and Across Overlapping Groups
Bayram, İ. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2785183 · Cited by 6
Iterative positive thresholding algorithm for non-negative sparse optimization
Zhang, L., Hu, Y., Yu, C. K. W., & Wang, J. (2018) · Optimization · doi:10.1080/02331934.2018.1470629 · Cited by 6
Yoshikawa, K., & Kawano, S. (2022) · Computational Statistics · doi:10.1007/s00180-022-01216-5 · Cited by 6
Group variable selection in the Andersen–Gill model for recurrent event data
Cai, K., Shen, H., & Lu, X. (2019) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2019.11.005 · Cited by 6
A Two-Part Framework for Estimating Individualized Treatment Rules From Semicontinuous Outcomes
Huling, J. D., Smith, M. A., & Chen, G. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1801449 · Cited by 6
Kim, R., Müller, S., & Garcia, T. P. (2021) · Biometrical Journal · doi:10.1002/bimj.202000312 · Cited by 6
Thi, H. A. L., Phan, D. N., & Dinh, T. P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.039 · Cited by 6
Wang, F., Padilla, O. H. M., Yu, Y., & Rinaldo, A. (2021) · Warwick Research Archive Portal (University of Warwick) · doi:10.48550/arxiv.2110.14298 · Cited by 6
Yu, T., Mohammadi, A., Tan, Y., Choong, P., & Oetomo, D. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3258225 · Cited by 6
Parametric and semiparametric reduced-rank regression with flexible sparsity
Lian, H., Feng, S., & Zhao, K. (2015) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2015.01.013 · Cited by 6
Scaled and square-root elastic net
Raninen, E., & Ollila, E. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952975 · Cited by 6
Flexible and Interpretable Models for Survival Data
Wu, J., & Witten, D. (2019) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2019.1592758 · Cited by 6
Dynamic portfolio selection with sector-specific regularization
Hafner, C. M., & Wang, L. (2022) · Econometrics and Statistics · doi:10.1016/j.ecosta.2022.01.001 · Cited by 6
Data-driven discovery of the heat equation in an induction machine via sparse regression
Goharoodi, S. K., Phuc, P. N., Dupré, L. R., & Crevecoeur, G. (2019) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2019.8754983 · Cited by 6
A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D ShapesPreprint
Wang, B., Sudijono, T., Kirveslahti, H., Gao, T., Boyer, D. M., Mukherjee, S., & Crawford, L. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/701391 · Cited by 6
Chen, Y., Trimborn, S., & Zhang, J. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3245031 · Cited by 6
Taheri, M., Lim, N., & Lederer, J. (2016) · arXiv (Cornell University) · Cited by 6
A new multiple kernel-based regularization method for identification of delay linear dynamic systems
Chen, X., Mao, Z., & Jia, R. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.103971 · Cited by 5
Uncertainty-aware multi-view evidence fusion for feature selection in brain network analysis
Chen, Y., Ding, W., Gao, S., Yang, J., Zhou, T., Hua, Q., & Fu, F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.104083 · Cited by 5
A convex-nonconvex strategy for grouped variable selection
Liu, X., Molstad, A. J., & Chi, E. C. (2023) · Electronic Journal of Statistics · doi:10.1214/23-ejs2167 · Cited by 5
Logistic regression with sparse common and distinctive covariates
Park, S., Ceulemans, E., & Deun, K. V. (2023) · Behavior Research Methods · doi:10.3758/s13428-022-02011-2 · Cited by 5
Li, Y., Ni, Y., Zheng, H., & Zhou, L. (2023) · Systems · doi:10.3390/systems11100502 · Cited by 5
Li, F., Hu, Y., Zhang, H., Deng, A., & Zurada, J. M. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3453549 · Cited by 5
Causality-driven candidate identification for reliable DNA methylation biomarker discovery
Tang, X., Guo, R., Mo, Z., Fu, W., & Qian, X. (2025) · Nature Communications · doi:10.1038/s41467-025-56054-y · Cited by 5
Quantum Algorithms for the Pathwise Lasso
Doriguello, J. F., Lim, D., Pun, C. S., Rebentrost, P., & Vaidya, T. S. (2025) · Quantum · doi:10.22331/q-2025-03-25-1674 · Cited by 5
A Coordinate-Descent-Based Approach to Solving the Sparse Group Elastic Net
Samarov, D. V., Allen, D. W., Hwang, J., Lee, Y. J., & Litorja, M. (2016) · Technometrics · doi:10.1080/00401706.2016.1273138 · Cited by 5
Explaining Gene Expression Using Twenty-One MicroRNAs
Asiaee, A., Abrams, Z. B., Nakayiza, S., Sampath, D., & Coombes, K. R. (2019) · Journal of Computational Biology · doi:10.1089/cmb.2019.0321 · Cited by 7
MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing
Wang, L., Du, P., & Jin, R. (2021) · Sensors · doi:10.3390/s21010243 · Cited by 5
Pasin, C., Moy, R. H., Reshef, R., & Yates, A. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-82562-0 · Cited by 5
Sparse-Group Non-convex Penalized Multi-Attribute Graphical Model Selection
TUGNAIT, J. K. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616255 · Cited by 5
Ramchoun, H., & Ettaouil, M. (2022) · Progress in Artificial Intelligence · doi:10.1007/s13748-022-00285-3 · Cited by 6
Sparse neural network regression with variable selection
Shin, J., Bak, K., & Koo, J. (2022) · Computational Intelligence · doi:10.1111/coin.12557 · Cited by 5
Deng, L., Chen, W., & Xiao, M. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3263506 · Cited by 7
Pathological Imaging-Assisted Cancer Gene–Environment Interaction Analysis
Fang, K., Li, J., Zhang, Q., Xu, Y., & Ma, S. (2023) · Biometrics · doi:10.1111/biom.13873 · Cited by 6
Using interpretable boosting algorithms for modeling environmental and agricultural data
Obster, F., Heumann, C., Bohle, H., & Pechan, P. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-39918-5 · Cited by 5
Identifying phenotype-associated subpopulations through LP_SGL
Li, J., Zhang, H., Mu, B., Zuo, H., & Zhou, K. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad424 · Cited by 5
Sparse Group Penalties for bi‐level variable selection
Buch, G., Schulz, A., Schmidtmann, I. M., Strauch, K., & Wild, P. S. (2024) · Biometrical Journal · doi:10.1002/bimj.202200334 · Cited by 5
Xia, Y., & Sun, J. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003121572 · Cited by 5
Integrated epigenomic exposure signature discovery
Schuetter, J., Minard-Smith, A. T., Hill, B., Beare, J. L., Vornholt, A., Burke, T. W., Murugan, V., Smith, A. K., Chandrasekaran, T., Shamma, H. J., et al. (2024) · Epigenomics · doi:10.1080/17501911.2024.2375187 · Cited by 5
Zhao, W., Zhang, R., & Liu, J. (2014) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.05.003 · Cited by 5
Feature Selection via Co-regularized Sparse-Group Lasso
Santos, P. L. A., Imangaliyev, S., Klamer Schutte, B., & Levin, E. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51469-7_10 · Cited by 5
Identification of gene pairs through penalized regression subject to constraints
Shen, R., Luo, L., & Jiang, H. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1872-9 · Cited by 5
The Broken Adaptive Ridge Procedure and Its Applications
Dai, L., Chen, K., & Li, G. (2018) · Statistica Sinica · doi:10.5705/ss.202018.0075 · Cited by 5
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Li, W., Lou, J., Zhou, S., & Lu, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32692-0_30 · Cited by 5
Doppler Signature Separation of Mixed O/X-Mode Over-The-Horizon Radar Signals
Ahmed, A., Zhang, Y. D., & Himed, B. (2020) · IEEE Radar Conference (RadarConf) · doi:10.1109/radarconf2043947.2020.9266392 · Cited by 5
Bayesian group selection with non-local priors
Li, W., & Chekouo, T. (2021) · Computational Statistics · doi:10.1007/s00180-021-01115-1 · Cited by 5
Bandi, A., R., B. S. M., Chatzinotas, S., & Ottersten, B. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3135701 · Cited by 5
Bayesian Interaction Selection Model for Multimodal Neuroimaging Data Analysis
Zhao, Y., Wu, B., & Kang, J. (2022) · Biometrics · doi:10.1111/biom.13648 · Cited by 5
Robust structured heterogeneity analysis approach for high‐dimensional data
Sun, Y., Luo, Z., & Fan, X. (2022) · Statistics in Medicine · doi:10.1002/sim.9414 · Cited by 5
Puliparambil, B. S., Tomal, J., & Yan, Y. (2022) · Biology · doi:10.3390/biology11101495 · Cited by 5
BSGS: Bayesian Sparse Group Selection
Lee, K., & Chen, R. (2015) · The R Journal · doi:10.32614/rj-2015-025 · Cited by 5
Unsupervised group feature selection for media classification
Zaharieva, M., Breiteneder, C. J., & Hudec, M. (2017) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-017-0126-y · Cited by 5
Wang, S., Huang, Y., Gong, L., Li, L., & Liu, C. (2016) · Advances in Mechanical Engineering · doi:10.1177/1687814016683085 · Cited by 5
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Shen, Z., Qian, H., Mu, T., & Zhang, C. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/378 · Cited by 5
Gao, W., & Li, X. (2017) · Chinese Astronomy and Astrophysics · doi:10.1016/j.chinastron.2017.08.004 · Cited by 5
Tran, N., Alipourfard, B., Abhyankar, V. V., Nguyen, K. T., Weidanz, J. A., & Gao, J. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037416 · Cited by 5
Ensembled sparse‐input hierarchical networks for high‐dimensional datasets
Feng, J., & Simon, N. (2022) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11579 · Cited by 5
Self-citationA Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data
Raghu, V. K., Ge, X., Narayanan, A. B. L., Shirer, D. J., Das, I., Benos, P. V., & Chrysanthis, P. K. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3019237 · Cited by 5
Screening Rules and its Complexity for Active Set Identification
Ndiaye, E., Fercoq, O., & Salmon, J. (2021) · Journal of convex analysis · doi:10.68381/jca28061 · Cited by 5
Metadata
- Authors
- Simon, N., Friedman, J., Hastie, T., & Tibshirani, R.
- Affiliations
- Stanford University (US)
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2013-01
- Volume
- 22
- Issue
- 2
- Pages
- 231-245
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W1987371344
- Cited by
- 1,380
- References
- 15
- Citations by year
- 2026: 82 · 2025: 94 · 2024: 125 · 2023: 98 · 2022: 120 · 2021: 152 · 2020: 132 · 2019: 135 · 2018: 93 · 2017: 116 · 2016: 84 · 2015: 71 · 2014: 52 · 2013: 22
- FWCI
- 44.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computational and Graphical Statistics (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Keywords
- sparse group lasso, high-dimensional linear regression, convex loss functions, regularization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
