A Sparse-Group Lasso

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

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

Regularization 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

Regularization 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

Model 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

A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen

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

How to Improve Immersive Experience?

Gao, Y., Wu, D., & Zhou, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3199666 · Cited by 14

Multi-task Learning with Adaptive Global Temporal Structure for Predicting Alzheimer's Disease Progression

Zhou, M., Zhang, Y., Liu, T., Yang, Y., & Yang, P. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557406 · Cited by 16

Solving OSCAR regularization problems by fast approximate proximal splitting algorithms

Zeng, X., & Figueiredo, M. A. T. (2014) · Digital Signal Processing · doi:10.1016/j.dsp.2014.03.010 · Cited by 14

Prediction of cancer drug sensitivity using high-dimensional omic features

Chen, T.-H., & Sun, W. (2016) · Biostatistics · doi:10.1093/biostatistics/kxw022 · Cited by 14

A group adaptive elastic-net approach for variable selection in high-dimensional linear regression

Hu, J., Huang, J., & Qiu, F. (2017) · Science China Mathematics · doi:10.1007/s11425-016-0071-x · Cited by 14

An automated approach towards sparse single-equation cointegration modelling

Smeekes, S., & Wijler, E. (2020) · Journal of Econometrics · doi:10.1016/j.jeconom.2020.07.021 · Cited by 14

Performing Sparse Regularization and Dimension Reduction Simultaneously in Multimodal Data Fusion

Yang, Z., Zhuang, X., Bird, C., Sreenivasan, K. R., Mishra, V. R., Banks, S. J., Cordes, D., & Initiative, T. A. D. N. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00642 · Cited by 14

Sparse estimation of multivariate Poisson log‐normal models from count data

Wu, H., Deng, X., & Ramakrishnan, N. (2018) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11370 · Cited by 14

Classification of MRI under the Presence of Disease Heterogeneity using Multi-Task Learning: Application to Bipolar Disorder

Wang, X., Zhang, T., Chaim, T. M., Zanetti, M. V., & Davatzikos, C. A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_16 · Cited by 14

Interpretable Convolution Methods for Learning Genomic Sequence MotifsPreprint

Ploenzke, M., & Irizarry, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/411934 · Cited by 14

Leading edge competition promotes context-dependent responses to receptor inputs to resolve directional dilemmas in neutrophil migration

Hadjitheodorou, A., Bell, G. R. R., Ellett, F. E., Irimia, D., Tibshirani, R. J., Collins, S. R., & Theriot, J. A. (2023) · Cell Systems · doi:10.1016/j.cels.2023.02.001 · Cited by 13

Compressed lightweight deep learning models for resource‐constrained Internet of things devices in the healthcare sector

Habib, G., & Qureshi, S. (2023) · Expert Systems · doi:10.1111/exsy.13269 · Cited by 13

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data

Margeloiu, A., Simidjievski, N., Lió, P., & Jamnik, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26090 · Cited by 13

Learning functional brain networks with heterogeneous connectivities for brain disease identification

Zhang, C., Ma, Y., Qiao, L., Zhang, L., & Liu, M. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106660 · Cited by 13

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

Musib, L. Q., Coletti, R., Lopes, M. B., Mouriño, H., & Carrasquinha, E. (2024) · BioData Mining · doi:10.1186/s13040-024-00401-0 · Cited by 13

Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach

Zeng, Q., Lu, X., Xu, J., & Yu, L. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103711 · Cited by 13

Supervised categorical principal component analysis for genome-wide association analyses

Lu, M., Lee, H., Hadley, D., Huang, J. Z., & Qian, X. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s1-s10 · Cited by 13

Two new approaches to compressed sensing exhibiting both robust sparse recovery and the grouping effect

Ahsen, M. E., & Vidyasagar, M. (2017) · Indian Control Conference · doi:10.1109/indiancc.2017.7846482 · Cited by 13

Structured sparse recovery algorithms for data decoding in media based modulation

Bandi, A., & Murthy, C. R. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7997435 · Cited by 13

A new Gibbs sampler for Bayesian lasso

Alhamzawi, R., & Ali, H. T. M. (2018) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2018.1508699 · Cited by 13

Structured Penalized Regression for Drug Sensitivity Prediction

Zhao, Z. Q., & Zucknick, M. (2020) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12400 · Cited by 13

Survival analysis on rare events using group-regularized multi-response Cox regression

Li, R., Tanigawa, Y., Justesen, J. M., Taylor, J., Hastie, T. J., Tibshirani, R. J., & Rivas, M. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab095 · Cited by 13

SURVFIT: Doubly sparse rule learning for survival data

Shakur, A. H., Huang, S., Qian, X., & Chang, X. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103691 · Cited by 13

Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Zhao, Z. Q., Wang, S., Zucknick, M., & Aittokallio, T. A. (2022) · iScience · doi:10.1016/j.isci.2022.104767 · Cited by 13

Influencer detection meets network autoregression — Influential regions in the bitcoin blockchain

Trimborn, S., Peng, H., & Chen, Y. (2024) · Journal of Empirical Finance · doi:10.1016/j.jempfin.2024.101529 · Cited by 13

Additive model selection

Amato, U., Antoniadis, A., & Feis, I. D. (2016) · Statistical Methods & Applications · doi:10.1007/s10260-016-0357-8 · Cited by 13

Integrative regression network for genomic association study

Vangimalla, R. R., Jeong, H.-H., & Sohn, K.-A. (2016) · BMC Medical Genomics · doi:10.1186/s12920-016-0192-7 · Cited by 13

Sparse principal component analysis for multiblock data and its extension to sparse multiple correspondence analysis

Bernard, A., Guinot, C., & Saporta, G. (2012) · HAL · Cited by 13

Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior

Thomson, W., Jabbari, S., TAYLOR, A. E. R., Arlt, W., & Smith, D. J. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2018.0572 · Cited by 13

An Iterative Sparse-Group Lasso

Laria, J. C., Aguilera‐Morillo, M. C., & Lillo, R. E. (2019) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2019.1573687 · Cited by 13

Gene selection of rat hepatocyte proliferation using adaptive sparse group lasso with weighted gene co-expression network analysis

Li, J., Wang, Y., Xiao, H., & Xu, C. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.04.010 · Cited by 13

Adaptive and reversed penalty for analysis of high-dimensional correlated data

Yang, Y., & Yang, H. (2020) · Applied Mathematical Modelling · doi:10.1016/j.apm.2020.11.004 · Cited by 13

A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step

Chen, J. (2021) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2020.1854763 · Cited by 13

Multi-auxiliary domain transfer learning for diagnosis of MCI conversion

Cheng, B., Zhu, B., & Pu, S. (2021) · Neurological Sciences · doi:10.1007/s10072-021-05568-6 · Cited by 13

Concave 1-norm group selection

Jiang, D., & Huang, J. (2014) · Biostatistics · doi:10.1093/biostatistics/kxu050 · Cited by 13

Data-Driven discovery of governing physical laws and their parametric dependencies in engineering, physics and biology

Kutz, J. N., Rudy, S., Alla, A., & Brunton, S. L. (2017) · IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · doi:10.1109/camsap.2017.8313100 · Cited by 13

A note on coding and standardization of categorical variables in (sparse) group lasso regression

Detmer, F. J., Cebral, J., & Slawski, M. (2019) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2019.08.003 · Cited by 13

Regularized joint estimation of related vector autoregressive models

Skripnikov, A., & Michailidis, G. C. (2019) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2019.05.007 · Cited by 13

Discussion of “Correlated variables in regression: Clustering and sparse estimation”

Bien, J., & Wegkamp, M. H. (2013) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2013.05.020 · Cited by 13

Fast non-coplanar beam orientation optimization based on group sparsityPreprint

O'Connor, D., Voronenko, Y., Nguyen, D. T. D., Yin, W., & Sheng, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.05308 · Cited by 13

View selection in multi-view stacking: choosing the meta-learner

van Loon, W., Fokkema, M., Szabó, B., & de Rooij, M. (2024) · Advances in Data Analysis and Classification · doi:10.1007/s11634-024-00587-5 · Cited by 12

Causal impact evaluation of occupational safety policies on firms’ default using machine learning uplift modelling

Barile, B., Forti, M., Marrocco, A., & Castaldo, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-60348-4 · Cited by 15

Tractometry of the Human Connectome Project: resources and insights

Kruper, J., Hagen, M. P., Rheault, F., Crane, I., Gilmore, A., Narayan, M., Motwani, K., Lila, E., Rorden, C., Yeatman, J. D., et al. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1389680 · Cited by 12

Exploring smart heat meter data: A co-clustering driven approach to analyse the energy use of single-family houses

Schaffer, M., Vera‐Valdés, J. E., & Marszal-Pomianowska, A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123586 · Cited by 13

Multi-Level Common Space Learning for Person Re-Identification

An, L., Qin, Z., Chen, X., & Yang, S. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2680118 · Cited by 12

Effect fusion using model-based clustering

Malsiner‐Walli, G., Pauger, D., & Wagner, H. (2018) · Statistical Modelling · doi:10.1177/1471082x17739058 · Cited by 12

Group Sparse CNNs for Question Classification with Answer Sets

Ma, M., Huang, L., Xiang, B., & Zhou, B. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2053 · Cited by 12

Survival analysis of failures based on Hawkes process with Weibull base intensity

Zhang, L.-N., Liu, J., & Zuo, X. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103709 · Cited by 12

Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening

Kato, H., Hanada, H., & Takeuchi, I. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3167993 · Cited by 13

WGRLR: A Weighted Group Regularized Logistic Regression for Cancer Diagnosis and Gene Selection

Song, X., Liang, K., & Li, J. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3203167 · Cited by 12

Low-Rank and Sparse Decomposition for Low-Query Decision-Based Adversarial Attacks

Esmaeili, A., Edraki, M., Rahnavard, N., Mian, A. S., & Shah, M. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3275737 · Cited by 13

A Two-Stage Method to Identify Joint Modules From Matched MicroRNA and mRNA Expression Data

Min, W., Liu, J., Luo, F., & Zhang, S. (2016) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2016.2556744 · Cited by 12

Distributed Scheduling of Network Connectivity Using Mobile Access Point Robots

Chatzipanagiotis, N., & Zavlanos, M. M. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2593041 · Cited by 12

Change-point estimation in high dimensional linear regression models via sparse group Lasso

Zhang, B., Geng, J., & Lai, L. (2015) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2015.7447090 · Cited by 12

Collinear groupwise feature selection via discrete fusion group regression

Kim, Y.-H., & Kim, S. B. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.013 · Cited by 12

Bayesian structure selection for vector autoregression model

Chu, C., Huang, M. L., Huang, S., & Chen, R. (2019) · Journal of Forecasting · doi:10.1002/for.2573 · Cited by 12

Maximum regularized likelihood estimators: A general prediction theory and applications

Zhuang, R., & Lederer, J. (2018) · Stat · doi:10.1002/sta4.186 · Cited by 12

An empirical threshold of selection probability for analysis of high-dimensional correlated data

Kim, K., Koo, J., & Sun, H. (2020) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2020.1739286 · Cited by 12

Two-stage adaptive integration of multi-source heterogeneous data based on an improved random subspace and prediction of default risk of microcredit

Huang, A., & Wu, F. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05489-z · Cited by 12

Smoothing Strategy Along with Conjugate Gradient Algorithm for Signal Reconstruction

Wu, C., Wang, J., Alcantara, J. H., & Chen, J.-S. (2021) · Journal of Scientific Computing · doi:10.1007/s10915-021-01440-z · Cited by 12

Dual Extrapolation for Sparse Generalized Linear ModelsPreprint

Massias, M., Vaiter, S., Gramfort, A., & Salmon, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.05830 · Cited by 12

Hierarchical autoencoder-based integration improves performance in multi-omics cancer survival models through soft modality selectionPreprint

Wissel, D., Rowson, D., & Boeva, V. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.16.460589 · Cited by 13

Screening Rules for Overlapping Group LassoPreprint

Lee, S., & P Xing, E. (2014) · arXiv (Cornell University) · doi:10.1184/r1/6476324 · Cited by 15

Compressive channel estimation and multi-user detection in C-RAN

He, Q., Quek, T. Q. S., Chen, Z., & Li, S. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996388 · Cited by 11

Instance-wise Feature Grouping

Masoomi, A., Wu, C., Zhao, T., Wang, Z., Castaldi, P. J., & Dy, J. (2020) · neural information processing systems · Cited by 11

Biomarker discovery from high-throughput data by connected network-constrained support vector machine

Li, L., & Liu, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120179 · Cited by 11

Evolutionary sparse learning reveals the shared genetic basis of convergent traits

Allard, J. B., Sharma, S., Patel, R., Sanderford, M., Tamura, K., Vucetic, S., Gerhard, G. S., & Kumar, S. (2025) · Nature Communications · doi:10.1038/s41467-025-58428-8 · Cited by 11

Reliable Gender Prediction Based on Users’ Video Viewing Behavior

Zhang, J., Du, K., Cheng, R., Wei, Z., Qin, C., You, H., & Hu, S. (2016) · Proceedings · doi:10.1109/icdm.2016.0076 · Cited by 11

A new linear convergence result for the iterative soft thresholding algorithm

Zhang, L., Hu, Y., Li, C., & Yao, J. (2017) · Optimization · doi:10.1080/02331934.2017.1318133 · Cited by 11

Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients

de Vos, F., Schouten, T. M., Koini, M., Bouts, M. J. R. J., Feis, R. A., Lechner, A., Schmidt, R., van Buchem, M. A., Verhey, F. R. J., Rikkert, M. G. M. O., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102303 · Cited by 12

A Learning Framework for Personalized Random Utility Maximization (RUM) Modeling of User Behavior

Feng, J., Zhu, X., Wang, F., Huang, S., & Chen, C. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3041411 · Cited by 11

Deep neural networks architecture driven by problem-specific information

Urda, D., Veredas, F. J., González-Enrique, J., Ruíz-Aguilar, J. J., Jerez, J. M., & Turias, I. J. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05702-7 · Cited by 11

Sparse group variable selection based on quantile hierarchical Lasso

Zhao, W., Zhang, R., & Liu, J. (2014) · Journal of Applied Statistics · doi:10.1080/02664763.2014.888541 · Cited by 11

Linking metabolic network features to phenotypes using sparse group lasso

Samal, S. S., Radulescu, O., Weber⋆, A., & Fröhlich, H. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx427 · Cited by 11

Variance Component Selection With Applications to Microbiome Taxonomic Data

Zhai, J., Kim, J., Knox, K. S., Twigg, H. L., Zhou, H., & Zhou, J. J. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.00509 · Cited by 11

Integrative linear discriminant analysis with guaranteed error rate improvement

Li, Q., & Li, L. (2018) · Biometrika · doi:10.1093/biomet/asy047 · Cited by 11

Change‐point detection in a linear model by adaptive fused quantile method

Ciuperca, G., & Maciak, M. (2019) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12412 · Cited by 11

Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis

Zheng, K. W., Bai, Y., Xiong, J., Tan, F., Yang, D., & Zhang, Y. (2020) · Sensors · doi:10.3390/s20195541 · Cited by 11

Online Estimation of Multiple Harmonic Signals

Elvander, F., Swärd, J., & Jakobsson, A. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2634118 · Cited by 11

A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

Barron, M., Zhang, S., & Li, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1113 · Cited by 11

Vector-Space Markov Random Fields via Exponential FamiliesPreprint

Tansey, W., Padilla, O. H. M., Suggala, A. S., & Ravikumar, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.05117 · Cited by 11

Models of anisotropic scattering for 3D SAR reconstruction

Sugavanam, N., & Ertin, E. (2022) · IEEE Radar Conference (RadarConf) · doi:10.1109/radarconf2248738.2022.9764209 · Cited by 10

Designing a symmetric classifier for image annotation using multi-layer sparse coding

Tariq, A., & Foroosh, H. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.11.002 · Cited by 10

New variable selection strategy for analysis of high-dimensional DNA methylation data

Choi, J.-Y., Kim, K., & Sun, H. (2018) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720018500105 · Cited by 10

Lung Cancer Classification and Gene Selection by Combining Affinity Propagation Clustering and Sparse Group Lasso

Li, J., Chang, M., Gao, Q., Song, X., & Gao, Z. (2019) · Current Bioinformatics · doi:10.2174/1574893614666191017103557 · Cited by 10

Statistical and Machine Learning Methods for eQTL Analysis

Chen, J., & Nodzak, C. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_7 · Cited by 10

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery

Wang, Y., Chen, H., Zheng, F., Xu, C., Gong, T., & Chen, Y. (2020) · neural information processing systems · Cited by 10

Simultaneous needle catheter selection and dwell time optimization for preplanning of high-dose-rate brachytherapy of prostate cancer

Wang, C., Gonzalez, Y., Shen, C., Hrycushko, B. A., & Jia, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abd00e · Cited by 10

Sparse smooth group L0∘L1/2 regularization method for convolutional neural networks

Quasdane, M., Ramchoun, H., & Masrour, T. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111327 · Cited by 11

Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer

Jatoi, A., Vierkant, R. A., Hawthorne, K. M., Block, M. S., Ramus, S. J., Larson, N. B., Fridley, B. L., & Goode, E. L. (2016) · Oncology · doi:10.1159/000442710 · Cited by 10

Sparse regression for selecting fluorescence wavelengths for accurate prediction of food properties

Higashi, H., ElMasry, G. M., & Nakauchi, S. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.03.008 · Cited by 10

Efficient Exploration of Many Variables and Interactions Using Regularized Regression

Barrett, T. S., & Lockhart, G. (2018) · Prevention Science · doi:10.1007/s11121-018-0963-9 · Cited by 10

Principal component of explained variance: An efficient and optimal data dimension reduction framework for association studies

Turgeon, M., Oualkacha, K., Ciampi, A., Miftah, H., Dehghan, G., Zanke, B. W., Benedet, A. L., Rosa‐Neto, P., Greenwood, C. M. T., Labbe, A., et al. (2016) · Statistical Methods in Medical Research · doi:10.1177/0962280216660128 · Cited by 10

Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling

Zhang, Y., Wang, L., Chen, X., & Jin, R. (2019) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icphys.2019.8780264 · Cited by 10

Bayesian indicator variable selection to incorporate hierarchical overlapping group structure in multi-omics applications

Zhu, L., Huo, Z., Ma, T., Oesterreich, S., & Tseng, G. C. (2019) · The Annals of Applied Statistics · doi:10.1214/19-aoas1271 · Cited by 10

Identifying disease‐associated biomarker network features through conditional graphical model

Xie, S., Li, X., McColgan, P., Scahill, R. I., Zeng, D., & Wang, Y. (2019) · Biometrics · doi:10.1111/biom.13201 · Cited by 10

Joint sparse principal component regression with robust property

Qi, K., Tu, J., & Yang, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115845 · Cited by 10

Joint Inference of Multiple Graphs with Hidden Variables from Stationary Graph Signals

Rey, S., Buciulea, A., Navarro, M., Segarra, S., & Marques, A. G. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747524 · Cited by 10

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Mohammadi, H. G., Gaillardon, P., & Micheli, G. D. (2016) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2016.2547908 · Cited by 10

Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models

Yang, K., & Maiti, T. (2020) · Machine Learning and Knowledge Extraction · doi:10.3390/make2010001 · Cited by 10

Sparse Group Selection Through Co-Adaptive PenaltiesPreprint

Fang, Z. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1111.4416 · Cited by 2

A note on the group lasso and a sparse group lassoPreprint

Friedman, J. H., Hastie, T., & Tibshirani, R. J. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1001.0736 · Cited by 645

Sparse optimization for nonconvex group penalized estimation

Lee, S., Oh, M., & Kim, Y. (2015) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2015.1026822 · Cited by 11

Sparse Group Selection Through Co-Adaptive Penalties Zhou FangPreprint

(2011)

Constrained Grouped Sparsity

Guo, Y., Gao, J., & Hong, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35101-3_37 · Cited by 1