{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T22:10:23Z","timestamp":1740694223861,"version":"3.38.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72021001","72371257","72001222"],"award-info":[{"award-number":["72021001","72371257","72001222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jing Ying Scholar Support Program, the Program for Innovation Research, and the Double First-class Project in Central University of Finance and Economics (CUFE)."}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s00500-025-10531-0","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T07:39:39Z","timestamp":1739432379000},"page":"1369-1387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Variable selection of multiple types of data: a PLS approach"],"prefix":"10.1007","volume":"29","author":[{"given":"Boao","family":"Kong","sequence":"first","affiliation":[]},{"given":"Huiwen","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8263-8598","authenticated-orcid":false,"given":"Shan","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"issue":"2","key":"10531_CR1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","volume":"44","author":"J Aitchison","year":"1982","unstructured":"Aitchison J (1982) The statistical analysis of compositional data. J Roy Stat Soc: Ser B (Methodol) 44(2):139\u2013160","journal-title":"J Roy Stat Soc: Ser B (Methodol)"},{"issue":"3","key":"10531_CR2","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1002\/cem.785","volume":"17","author":"M Barker","year":"2003","unstructured":"Barker M, Rayens W (2003) Partial least squares for discrimination. J Chem: J Chem Soc 17(3):166\u2013173","journal-title":"J Chem: J Chem Soc"},{"issue":"3","key":"10531_CR3","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s11634-022-00520-8","volume":"17","author":"B Beranger","year":"2023","unstructured":"Beranger B, Lin H, Sisson S (2023) New models for symbolic data analysis. Adv Data Anal Classif 17(3):659\u2013699","journal-title":"Adv Data Anal Classif"},{"key":"10531_CR4","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jmva.2018.04.008","volume":"170","author":"JR Berrendero","year":"2019","unstructured":"Berrendero JR, Bueno-Larraz B, Cuevas A (2019) An rkhs model for variable selection in functional linear regression. J Multivar Anal 170:25\u201345","journal-title":"J Multivar Anal"},{"issue":"1","key":"10531_CR5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1093\/bib\/bbl016","volume":"8","author":"A-L Boulesteix","year":"2007","unstructured":"Boulesteix A-L, Strimmer K (2007) Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform 8(1):32\u201344","journal-title":"Brief Bioinform"},{"key":"10531_CR6","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104969","volume":"139","author":"AB Buriro","year":"2021","unstructured":"Buriro AB, Ahmed B, Baloch G, Ahmed J, Shoorangiz R, Weddell SJ, Jones RD (2021) Classification of alcoholic eeg signals using wavelet scattering transform-based features. Comput Biol Med 139:104969","journal-title":"Comput Biol Med"},{"key":"10531_CR7","doi-asserted-by":"crossref","DOI":"10.1016\/j.saa.2021.120652","volume":"268","author":"J Cheng","year":"2022","unstructured":"Cheng J, Sun J, Yao K, Min X, Cao Y (2022) A variable selection method based on mutual information and variance inflation factor. Spectrochim Acta Part A Mol Biomol Spectrosc 268:120652","journal-title":"Spectrochim Acta Part A Mol Biomol Spectrosc"},{"issue":"1","key":"10531_CR8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/j.1467-9868.2009.00723.x","volume":"72","author":"H Chun","year":"2010","unstructured":"Chun H, Kele\u015f S (2010) Sparse partial least squares regression for simultaneous dimension reduction and variable selection. J R Stat Soc: Ser B (Stat Methodol) 72(1):3\u201325","journal-title":"J R Stat Soc: Ser B (Stat Methodol)"},{"key":"10531_CR9","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2020.113325","volume":"135","author":"K Coussement","year":"2020","unstructured":"Coussement K, Phan M, De Caigny A, Benoit DF, Raes A (2020) Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model. Decis Support Syst 135:113325","journal-title":"Decis Support Syst"},{"issue":"6","key":"10531_CR10","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.jom.2012.06.002","volume":"30","author":"David Xiaosong Peng and Fujun Lai","year":"2012","unstructured":"David Xiaosong Peng and Fujun Lai (2012) Using partial least squares in operations management research: A practical guideline and summary of past research. J Oper Manag 30(6):467\u2013480","journal-title":"J Oper Manag"},{"key":"10531_CR11","doi-asserted-by":"crossref","unstructured":"De\u00a0Carvalho Francisco de AT, Balzanella A, Irpino A, Verde R (2021) Co-clustering algorithms for distributional data with automated variable weighting. Inform Sci 549:87\u2013115","DOI":"10.1016\/j.ins.2020.11.018"},{"issue":"2","key":"10531_CR12","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1002\/sam.11260","volume":"8","author":"S Dias","year":"2015","unstructured":"Dias S, Brito P (2015) Linear regression model with histogram-valued variables. Stat Anal Data Min: ASA Data Sci J 8(2):75\u2013113","journal-title":"Stat Anal Data Min: ASA Data Sci J"},{"issue":"3","key":"10531_CR13","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1023\/A:1023818214614","volume":"35","author":"JJ Egozcue","year":"2003","unstructured":"Egozcue JJ, Pawlowsky-Glahn V, Mateu-Figueras G, Barcelo-Vidal C (2003) Isometric logratio transformations for compositional data analysis. Math Geol 35(3):279\u2013300","journal-title":"Math Geol"},{"key":"10531_CR14","unstructured":"Fan J, Li R (2006) Statistical challenges with high dimensionality: feature selection in knowledge discovery. arXiv preprint math\/0602133"},{"issue":"1","key":"10531_CR15","first-page":"101","volume":"20","author":"J Fan","year":"2010","unstructured":"Fan J, Lv J (2010) A selective overview of variable selection in high dimensional feature space. Stat Sin 20(1):101","journal-title":"Stat Sin"},{"key":"10531_CR16","unstructured":"Ferraty F, Vieu P (2006) Nonparametric functional data analysis: theory and practice. vol\u00a076. Springer"},{"issue":"6","key":"10531_CR17","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1093\/bioinformatics\/btt608","volume":"30","author":"TP Garcia","year":"2014","unstructured":"Garcia TP, M\u00fcller S, Carroll RJ, Walzem RL (2014) Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data. Bioinformatics 30(6):831\u2013837","journal-title":"Bioinformatics"},{"issue":"02","key":"10531_CR18","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1142\/S0218339009002831","volume":"17","author":"I Gonz\u00e1lez","year":"2009","unstructured":"Gonz\u00e1lez I, D\u00e9jean S, Martin Pascal GP, Gon\u00e7alves O, Besse P, Alain B (2009) Highlighting relationships between heterogeneous biological data through graphical displays based on regularized canonical correlation analysis. J Biol Syst 17(02):173\u2013199","journal-title":"J Biol Syst"},{"issue":"5","key":"10531_CR19","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s11004-018-9754-x","volume":"51","author":"M Greenacre","year":"2019","unstructured":"Greenacre M (2019) Variable selection in compositional data analysis using pairwise logratios. Math Geosci 51(5):649\u2013682","journal-title":"Math Geosci"},{"key":"10531_CR20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.compedu.2013.10.012","volume":"72","author":"AC Hachey","year":"2014","unstructured":"Hachey AC, Wladis CW, Conway KM (2014) Do prior online course outcomes provide more information than gpa alone in predicting subsequent online course grades and retention? an observational study at an urban community college. Comput Educ 72:59\u201367","journal-title":"Comput Educ"},{"key":"10531_CR21","doi-asserted-by":"crossref","unstructured":"Hall P, Horowitz JL (2007) Methodology and convergence rates for functional linear regression","DOI":"10.1214\/009053606000000957"},{"key":"10531_CR22","doi-asserted-by":"crossref","unstructured":"Hayden EP, Wiegand RE, Meyer ET, Bauer LO, O\u2019connor SJ, Nurnberger Jr JI , Chorlian DB, Porjesz B, Begleiter H(2006) Patterns of regional brain activity in alcohol-dependent subjects. Alcohol: Clin Exp Res 30 (12):1986\u20131991","DOI":"10.1111\/j.1530-0277.2006.00244.x"},{"issue":"4","key":"10531_CR23","doi-asserted-by":"crossref","first-page":"4578","DOI":"10.1103\/PhysRevE.55.4578","volume":"55","author":"L Ingber","year":"1997","unstructured":"Ingber L (1997) Statistical mechanics of neocortical interactions: Canonical momenta indicatorsof electroencephalography. Phys Rev E 55(4):4578","journal-title":"Phys Rev E"},{"key":"10531_CR24","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10639-020-10230-3","volume":"26","author":"A Khan","year":"2021","unstructured":"Khan A, Ghosh SK (2021) Student performance analysis and prediction in classroom learning: a review of educational data mining studies. Educ Inform Technol 26:205\u2013240","journal-title":"Educ Inform Technol"},{"key":"10531_CR25","doi-asserted-by":"crossref","unstructured":"Kim-Anh LC, Rossouw D, Robert-Grani\u00e9 C, Besse P (2008) A sparse pls for variable selection when integrating omics data. Stat Appl Genet Mol Biol 7(1)","DOI":"10.2202\/1544-6115.1390"},{"issue":"1","key":"10531_CR26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-12-1","volume":"12","author":"LC Kim-Anh","year":"2011","unstructured":"Kim-Anh LC, Boitard S, Besse P (2011) Sparse pls discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinform 12(1):1\u201317","journal-title":"BMC Bioinform"},{"issue":"3","key":"10531_CR27","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.neubiorev.2006.10.004","volume":"31","author":"GG Knyazev","year":"2007","unstructured":"Knyazev GG (2007) Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci Biobehav Rev 31(3):377\u2013395","journal-title":"Neurosci Biobehav Rev"},{"key":"10531_CR28","doi-asserted-by":"crossref","unstructured":"Li B, Kim MK, Altman N (2010) On dimension folding of matrix-or array-valued statistical objects","DOI":"10.1214\/09-AOS737"},{"issue":"1","key":"10531_CR29","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/bioinformatics\/btv535","volume":"32","author":"B Liquet","year":"2016","unstructured":"Liquet B, De Micheaux PL, Hejblum BP, Thi\u00e9baut R (2016) Group and sparse group partial least square approaches applied in genomics context. Bioinformatics 32(1):35\u201342","journal-title":"Bioinformatics"},{"issue":"2","key":"10531_CR30","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1002\/for.1218","volume":"31","author":"JM Mat\u00edas","year":"2012","unstructured":"Mat\u00edas JM, Reboredo JC (2012) Forecasting performance of nonlinear models for intraday stock returns. J Forecast 31(2):172\u2013188","journal-title":"J Forecast"},{"key":"10531_CR31","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.knosys.2015.12.016","volume":"97","author":"Y Meng","year":"2016","unstructured":"Meng Y, Liang J, Qian Y (2016) Comparison study of orthonormal representations of functional data in classification. Knowl-Based Syst 97:224\u2013236","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"10531_CR32","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s40747-021-00383-0","volume":"8","author":"Z Mingyu","year":"2022","unstructured":"Mingyu Z, Sutong W, Yanzhang W, Dujuan W (2022) An interpretable prediction method for university student academic crisis warning. Complex Intell Syst 8(1):323\u2013336","journal-title":"Complex Intell Syst"},{"key":"10531_CR33","doi-asserted-by":"crossref","unstructured":"Pawlowsky-Glahn V, Egozcue JJ, Raimon T-D (2015) Modeling and analysis of compositional data. John Wiley & Sons","DOI":"10.1002\/9781119003144"},{"issue":"525","key":"10531_CR34","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1080\/01621459.2017.1390466","volume":"114","author":"X Qiao","year":"2019","unstructured":"Qiao X, Guo S, James GM (2019) Functional graphical models. J Am Stat Assoc 114(525):211\u2013222","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"10531_CR35","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12532-013-0051-x","volume":"5","author":"Z Qin","year":"2013","unstructured":"Qin Z, Scheinberg K, Goldfarb D (2013) Efficient block-coordinate descent algorithms for the group lasso. Math Program Comput 5(2):143\u2013169","journal-title":"Math Program Comput"},{"key":"10531_CR36","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1007\/BF02293704","volume":"47","author":"JO Ramsay","year":"1982","unstructured":"Ramsay JO (1982) When the data are functions. Psychometrika 47:379\u2013396","journal-title":"Psychometrika"},{"key":"10531_CR37","doi-asserted-by":"crossref","unstructured":"Ramsay JO, Silverman BW (2005) Principal components analysis for functional data. Functional data analysis, pp 147\u2013172","DOI":"10.1007\/b98888"},{"key":"10531_CR38","doi-asserted-by":"crossref","unstructured":"Rodrigues Jardel das C, Filho Pedro PR, Peixoto\u00a0Jr E, Kumar A, de\u00a0Albuquerque VHC (2019) Classification of eeg signals to detect alcoholism using machine learning techniques. Pattern Recogn Lett 125:140\u2013149","DOI":"10.1016\/j.patrec.2019.04.019"},{"key":"10531_CR39","volume":"223","author":"L Shan","year":"2021","unstructured":"Shan L, Zhao J, Wang H (2021) Md-mbpls: a novel explanatory model in computational social science. Knowl-Based Syst 223:107023","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"10531_CR40","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1140\/epjds\/s13688-022-00322-0","volume":"11","author":"L Shan","year":"2022","unstructured":"Shan L, Zhao J, Wang H (2022) Academic failures and co-location social networks in campus. EPJ Data Sci 11(1):10","journal-title":"EPJ Data Sci"},{"key":"10531_CR41","doi-asserted-by":"crossref","unstructured":"Susin A, Wang Y, Kim-Anh LC, Calle ML (2020) Variable selection in microbiome compositional data analysis. NAR Genomics and Bioinformatics, 2(2): lqaa029, 05. ISSN 2631-9268. 10.1093\/nargab\/lqaa029","DOI":"10.1093\/nargab\/lqaa029"},{"issue":"23","key":"10531_CR42","doi-asserted-by":"crossref","first-page":"3338","DOI":"10.1002\/sim.7821","volume":"37","author":"M Sutton","year":"2018","unstructured":"Sutton M, Thi\u00e9baut R, Liquet B (2018) Sparse partial least squares with group and subgroup structure. Stat Med 37(23):3338\u20133356","journal-title":"Stat Med"},{"key":"10531_CR43","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s004770100077","volume":"15","author":"Vera Pawlowsky-Glahn and Juan Jos\u00e9 Egozcue","year":"2001","unstructured":"Vera Pawlowsky-Glahn and Juan Jos\u00e9 Egozcue (2001) Geometric approach to statistical analysis on the simplex. Stoch Env Res Risk Assess 15:384\u2013398","journal-title":"Stoch Env Res Risk Assess"},{"key":"10531_CR44","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1023\/A:1014890722372","volume":"34","author":"Vera Pawlowsky-Glahn and Juan Jos\u00e9 Egozcue","year":"2002","unstructured":"Vera Pawlowsky-Glahn and Juan Jos\u00e9 Egozcue (2002) Blu estimators and compositional data. Math Geol 34:259\u2013274","journal-title":"Math Geol"},{"key":"10531_CR45","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.neucom.2013.05.025","volume":"122","author":"H Wang","year":"2013","unstructured":"Wang H, Shangguan L, Junjie W, Guan R (2013) Multiple linear regression modeling for compositional data. Neurocomputing 122:490\u2013500","journal-title":"Neurocomputing"},{"issue":"1","key":"10531_CR46","first-page":"8","volume":"42","author":"H Wang","year":"2016","unstructured":"Wang H, Huang L, Wang S (2016a) Generalized linear regression model based on functional data analysis. J Beijing Univ Aeronaut Astronaut 42(1):8\u201312","journal-title":"J Beijing Univ Aeronaut Astronaut"},{"key":"10531_CR47","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1146\/annurev-statistics-041715-033624","volume":"3","author":"J-L Wang","year":"2016","unstructured":"Wang J-L, Chiou J-M, M\u00fcller H-G (2016b) Functional data analysis. Ann Rev Stat Appl 3:257\u2013295","journal-title":"Ann Rev Stat Appl"},{"key":"10531_CR48","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.knosys.2018.10.035","volume":"164","author":"H Wang","year":"2019","unstructured":"Wang H, Shan L, Zhao J (2019) Aggregating multiple types of complex data in stock market prediction: A model-independent framework. Knowl-Based Syst 164:193\u2013204","journal-title":"Knowl-Based Syst"},{"issue":"538","key":"10531_CR49","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1080\/01621459.2020.1820344","volume":"117","author":"J Wang","year":"2022","unstructured":"Wang J, Wong Raymond KW, Zhang X (2022) Low-rank covariance function estimation for multidimensional functional data. J Am Stat Assoc 117(538):809\u2013822","journal-title":"J Am Stat Assoc"},{"issue":"5","key":"10531_CR50","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1007\/s11222-020-09955-5","volume":"30","author":"T Whitaker","year":"2020","unstructured":"Whitaker T, Beranger B, Sisson SA (2020) Composite likelihood methods for histogram-valued random variables. Stat Comput 30(5):1459\u20131477","journal-title":"Stat Comput"},{"key":"10531_CR51","unstructured":"Wold H (1966) Estimation of principal components and related models by iterative least squares. Multivariate analysis, pp 391\u2013420"},{"key":"10531_CR52","doi-asserted-by":"crossref","first-page":"6740","DOI":"10.1109\/TNNLS.2023.3249767","volume":"34","author":"X Wu","year":"2023","unstructured":"Wu X, Jiang B, Wang X, Ban T, Chen H (2023a) Feature selection in the data stream based on incremental markov boundary learning. IEEE Trans Neural Netw Learn Syst 34:6740","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10531_CR53","doi-asserted-by":"crossref","unstructured":"Wu X, Jiang B, Tianhao W, Chen H (2023b) Practical markov boundary learning without strong assumptions. Proc AAAI Conf Artif Intell 37:10388\u201310398","DOI":"10.1609\/aaai.v37i9.26236"},{"key":"10531_CR54","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.chb.2019.04.022","volume":"98","author":"X Xing","year":"2019","unstructured":"Xing X, Wang J, Peng H, Ruilin W (2019) Prediction of academic performance associated with internet usage behaviors using machine learning algorithms. Comput Hum Behav 98:166\u2013173","journal-title":"Comput Hum Behav"},{"issue":"4","key":"10531_CR55","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1177\/15500594221076347","volume":"53","author":"L Yang","year":"2022","unstructured":"Yang L, Yujie C, Gorka F-G, Veronica S, Judith L, Wiers RW, Ridderinkhof KR (2022) Resting-state eeg, substance use and abstinence after chronic use: a systematic review. Clin EEG Neurosci 53(4):344\u2013366","journal-title":"Clin EEG Neurosci"},{"issue":"1","key":"10531_CR56","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","volume":"68","author":"M Yuan","year":"2006","unstructured":"Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc: Ser B (Stat Methodol) 68(1):49\u201367","journal-title":"J R Stat Soc: Ser B (Stat Methodol)"},{"issue":"6","key":"10531_CR57","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/0361-9230(95)02023-5","volume":"38","author":"XL Zhang","year":"1995","unstructured":"Zhang XL, Begleiter H, Porjesz B, Wang W, Litke A (1995) Event related potentials during object recognition tasks. Brain Res Bull 38(6):531\u2013538","journal-title":"Brain Res Bull"},{"issue":"1","key":"10531_CR58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/sjos.12395","volume":"47","author":"X Zhang","year":"2020","unstructured":"Zhang X, Beranger B, Sisson SA (2020) Constructing likelihood functions for interval-valued random variables. Scandin J Stat 47(1):1\u201335","journal-title":"Scandin J Stat"},{"key":"10531_CR59","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.ins.2022.07.064","volume":"609","author":"Q Zhao","year":"2022","unstructured":"Zhao Q, Wang H, Shan L (2022) M-ldq feature embedding and regression modeling for distribution-valued data. Inf Sci 609:121\u2013152","journal-title":"Inf Sci"},{"issue":"542","key":"10531_CR60","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01621459.2022.2152342","volume":"118","author":"W Zhong","year":"2023","unstructured":"Zhong W, Qian C, Liu W, Zhu L, Li R (2023) Feature screening for interval-valued response with application to study association between posted salary and required skills. J Am Stat Assoc 118(542):805\u2013817","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"10531_CR61","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/rssb.12031","volume":"76","author":"H Zhou","year":"2014","unstructured":"Zhou H, Li L (2014) Regularized matrix regression. J R Stat Soc: Ser B, Stat Methodol 76(2):463","journal-title":"J R Stat Soc: Ser B, Stat Methodol"},{"key":"10531_CR62","doi-asserted-by":"publisher","unstructured":"Zhou Nengfeng, Zhu Ji (2010) Group variable selection via a hierarchical lasso and its oracle property. Stat Interface 3(4):557\u2013574. https:\/\/doi.org\/10.4310\/SII.2010.v3.n4.a13","DOI":"10.4310\/SII.2010.v3.n4.a13"},{"key":"10531_CR63","doi-asserted-by":"publisher","unstructured":"Zhou Zhenkun, Xu Ke, Zhao Jichang (2018) Tales of emotion and stock in China: volatility, causality and prediction. World Wide Web 21(4):1093\u20131116. https:\/\/doi.org\/10.1007\/s11280-017-0495-4","DOI":"10.1007\/s11280-017-0495-4"},{"issue":"1","key":"10531_CR64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/gb-2013-14-1-r1","volume":"23","author":"H Zhou","year":"2022","unstructured":"Zhou H, He K, Chen J, Zhang X (2022) Linda: linear models for differential abundance analysis of microbiome compositional data. Genome Biol 23(1):1\u201323","journal-title":"Genome Biol"},{"key":"10531_CR65","doi-asserted-by":"crossref","unstructured":"Zhou M, Ma M, Zhang Y, SuiA K, Pei D, Moscibroda T (2016) Edum: classroom education measurements via large-scale wifi networks. In: Proceedings of the 2016 acm international joint conference on pervasive and ubiquitous computing, pp 316\u2013327","DOI":"10.1145\/2971648.2971657"},{"key":"10531_CR66","unstructured":"Zhu H, Strawn N, Dunson DB (2016) Bayesian graphical models for multivariate functional data"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10531-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T21:42:42Z","timestamp":1740692562000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10531-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":66,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10531"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10531-0","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"6 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}