{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:40:11Z","timestamp":1742373611039,"version":"3.40.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Vanier Canada Graduate Scholarship"},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013531","name":"E.W.R. Steacie Memorial Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013531","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Classif"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s00357-024-09479-x","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T10:02:23Z","timestamp":1720778543000},"page":"113-133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions"],"prefix":"10.1007","volume":"42","author":[{"given":"Alexa A.","family":"Sochaniwsky","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5487-8965","authenticated-orcid":false,"given":"Michael P. B.","family":"Gallaugher","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2482-523X","authenticated-orcid":false,"given":"Paul D.","family":"McNicholas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"9479_CR1","volume-title":"R: A language and environment for statistical computing","author":"R Core Team","year":"2023","unstructured":"R Core Team. (2023). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing."},{"key":"9479_CR2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1017\/S0370164600024871","volume":"45","author":"AC Aitken","year":"1926","unstructured":"Aitken, A. C. (1926). A series formula for the roots of algebraic and transcendental equations. Proceedings of the Royal Society of Edinburgh, 45, 14\u201322.","journal-title":"Proceedings of the Royal Society of Edinburgh"},{"issue":"3","key":"9479_CR3","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s11222-010-9175-2","volume":"21","author":"JL Andrews","year":"2011","unstructured":"Andrews, J. L., & McNicholas, P. D. (2011). Extending mixtures of multivariate t-factor analyzers. Statistics and Computing, 21(3), 361\u2013373.","journal-title":"Statistics and Computing"},{"issue":"5","key":"9479_CR4","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s11222-011-9272-x","volume":"22","author":"JL Andrews","year":"2012","unstructured":"Andrews, J. L., & McNicholas, P. D. (2012). Model-based clustering, classification, and discriminant analysis via mixtures of multivariate $$t$$-distributions: The $$t$$EIGEN family. Statistics and Computing, 22(5), 1021\u20131029.","journal-title":"Statistics and Computing"},{"issue":"1","key":"9479_CR5","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.csda.2010.05.019","volume":"55","author":"JL Andrews","year":"2011","unstructured":"Andrews, J. L., McNicholas, P. D., & Subedi, S. (2011). Model-based classification via mixtures of multivariate t-distributions. Computational Statistics and Data Analysis, 55(1), 520\u2013529.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"3","key":"9479_CR6","doi-asserted-by":"crossref","first-page":"803","DOI":"10.2307\/2532201","volume":"49","author":"JD Banfield","year":"1993","unstructured":"Banfield, J. D., & Raftery, A. E. (1993). Model-based Gaussian and non-Gaussian clustering. Biometrics, 49(3), 803\u2013821.","journal-title":"Biometrics"},{"key":"9479_CR7","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1214\/aoms\/1177697196","volume":"41","author":"LE Baum","year":"1970","unstructured":"Baum, L. E., Petrie, T., Soules, G., & Weiss, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics, 41, 164\u2013171.","journal-title":"Annals of Mathematical Statistics"},{"issue":"1","key":"9479_CR8","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11634-013-0155-1","volume":"8","author":"S Bhattacharya","year":"2014","unstructured":"Bhattacharya, S., & McNicholas, P. D. (2014). A LASSO-penalized BIC for mixture model selection. Advances in Data Analysis and Classification, 8(1), 45\u201361.","journal-title":"Advances in Data Analysis and Classification"},{"key":"9479_CR9","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/BF01720593","volume":"46","author":"D B\u00f6hning","year":"1994","unstructured":"B\u00f6hning, D., Dietz, E., Schaub, R., Schlattmann, P., & Lindsay, B. (1994). The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family. Annals of the Institute of Statistical Mathematics, 46, 373\u2013388.","journal-title":"Annals of the Institute of Statistical Mathematics"},{"issue":"1","key":"9479_CR10","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.csda.2007.02.009","volume":"52","author":"C Bouveyron","year":"2007","unstructured":"Bouveyron, C., Girard, S., & Schmid, C. (2007). High-dimensional data clustering. Computational Statistics and Data Analysis, 52(1), 502\u2013519.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"2","key":"9479_CR11","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/cjs.11246","volume":"43","author":"RP Browne","year":"2015","unstructured":"Browne, R. P., & McNicholas, P. D. (2015). A mixture of generalized hyperbolic distributions. Canadian Journal of Statistics, 43(2), 176\u2013198.","journal-title":"Canadian Journal of Statistics"},{"issue":"3","key":"9479_CR12","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1007\/s00357-022-09421-z","volume":"39","author":"A Casa","year":"2022","unstructured":"Casa, A., Cappozzo, A., & Fop, M. (2022). Group-wise shrinkage estimation in penalized model-based clustering. Journal of Classification, 39(3), 648\u2013674.","journal-title":"Journal of Classification"},{"issue":"5","key":"9479_CR13","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/0031-3203(94)00125-6","volume":"28","author":"G Celeux","year":"1995","unstructured":"Celeux, G., & Govaert, G. (1995). Gaussian parsimonious clustering models. Pattern Recognition, 28(5), 781\u2013793.","journal-title":"Pattern Recognition"},{"key":"9479_CR14","doi-asserted-by":"crossref","unstructured":"Clark, K. M., & McNicholas, P. D. (2024). Finding outliers in Gaussian model-based clustering. Journal of Classification,41(3),.","DOI":"10.1007\/s00357-024-09473-3"},{"issue":"4","key":"9479_CR15","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1111\/biom.12351","volume":"71","author":"UJ Dang","year":"2015","unstructured":"Dang, U. J., Browne, R. P., & McNicholas, P. D. (2015). Mixtures of multivariate power exponential distributions. Biometrics, 71(4), 1081\u20131089.","journal-title":"Biometrics"},{"issue":"1","key":"9479_CR16","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s00357-022-09427-7","volume":"40","author":"UJ Dang","year":"2023","unstructured":"Dang, U. J., Gallaugher, M. P. B., Browne, R. P., & McNicholas, P. D. (2023). Model-based clustering and classification using mixtures of multivariate skewed power exponential distributions. Journal of Classification, 40(1), 145\u2013167.","journal-title":"Journal of Classification"},{"key":"9479_CR17","unstructured":"De\u00a0Moivre, A. (1730). Miscellanea Analytica. London: Tonson and Watts."},{"issue":"1","key":"9479_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1\u201338.","journal-title":"Journal of the Royal Statistical Society: Series B"},{"issue":"3","key":"9479_CR19","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1007\/s00357-022-09417-9","volume":"39","author":"Y Fang","year":"2022","unstructured":"Fang, Y., Karlis, D., & Subedi, S. (2022). Infinite mixtures of multivariate normal-inverse Gaussian distributions for clustering of skewed data. Journal of Classification, 39(3), 510\u2013552.","journal-title":"Journal of Classification"},{"issue":"6","key":"9479_CR20","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TPAMI.2013.216","volume":"36","author":"BC Franczak","year":"2014","unstructured":"Franczak, B. C., Browne, R. P., & McNicholas, P. D. (2014). Mixtures of shifted asymmetric Laplace distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6), 1149\u20131157.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"9479_CR21","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","volume":"9","author":"J Friedman","year":"2008","unstructured":"Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432\u2013441.","journal-title":"Biostatistics"},{"issue":"2","key":"9479_CR22","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s11634-019-00377-4","volume":"14","author":"MPB Gallaugher","year":"2020","unstructured":"Gallaugher, M. P. B., & McNicholas, P. D. (2020). Mixtures of skewed matrix variate bilinear factor analyzers. Advances in Data Analysis and Classification, 14(2), 415\u2013434.","journal-title":"Advances in Data Analysis and Classification"},{"issue":"2","key":"9479_CR23","first-page":"413","volume":"31","author":"MPB Gallaugher","year":"2022","unstructured":"Gallaugher, M. P. B., Tomarchio, S. D., McNicholas, P. D., & Punzo, A. (2022). Model-based clustering via skewed matrix-variate cluster-weighted models. Journal of Statistical Computation and Simulation, 31(2), 413\u2013421.","journal-title":"Journal of Statistical Computation and Simulation"},{"key":"9479_CR24","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1007\/s11222-020-09950-w","volume":"30","author":"LA Garcia-Escudero","year":"2020","unstructured":"Garcia-Escudero, L. A., Mayo-Iscar, A., & Riani, M. (2020). Model-based clustering with determinant-and-shape constraint. Statistics and Computing, 30, 1363\u20131380.","journal-title":"Statistics and Computing"},{"issue":"1","key":"9479_CR25","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193\u2013218.","journal-title":"Journal of Classification"},{"issue":"1","key":"9479_CR26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11222-008-9072-0","volume":"19","author":"D Karlis","year":"2009","unstructured":"Karlis, D., & Santourian, A. (2009). Model-based clustering with non-elliptically contoured distributions. Statistics and Computing, 19(1), 73\u201383.","journal-title":"Statistics and Computing"},{"key":"9479_CR27","unstructured":"Krishnamurthy, A. (2011). High-dimensional clustering with sparse Gaussian mixture models. Unpublished manuscript."},{"key":"9479_CR28","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s11222-012-9362-4","volume":"24","author":"S Lee","year":"2014","unstructured":"Lee, S., & McLachlan, G. J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24, 181\u2013202.","journal-title":"Statistics and Computing"},{"issue":"3","key":"9479_CR29","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s11222-009-9128-9","volume":"20","author":"T-I Lin","year":"2010","unstructured":"Lin, T.-I. (2010). Robust mixture modeling using multivariate skew t distributions. Statistics and Computing, 20(3), 343\u2013356.","journal-title":"Statistics and Computing"},{"key":"9479_CR30","doi-asserted-by":"crossref","unstructured":"Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. In NSF-CBMS Regional Conference Series in Probability and Statistics, Volume\u00a05. California: Institute of Mathematical Statistics: Hayward.","DOI":"10.1214\/cbms\/1462106013"},{"key":"9479_CR31","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.spl.2014.01.015","volume":"88","author":"T-I Lin","year":"2014","unstructured":"Lin, T.-I., McNicholas, P. D., & Hsiu, J. H. (2014). Capturing patterns via parsimonious t mixture models. Statistics and Probability Letters, 88, 80\u201387.","journal-title":"Statistics and Probability Letters"},{"key":"9479_CR32","doi-asserted-by":"crossref","unstructured":"McLaughlin, P., Franczak, B.\u00a0C., & Kashlak, A.\u00a0B. (2024). Unsupervised classification with a family of parsimonious contaminated shifted asymmetric Laplace mixtures. Journal of Classification. In press.","DOI":"10.1007\/s00357-023-09460-0"},{"key":"9479_CR33","unstructured":"McNicholas, P. D. (2016a). Mixture model-based classification. Boca Raton: Chapman & Hall\/CRC Press."},{"key":"9479_CR34","doi-asserted-by":"crossref","unstructured":"McNicholas, P. D. (2016b). Model-based clustering. Journal of Classification, 33(3), 331\u2013373.","DOI":"10.1007\/s00357-016-9211-9"},{"issue":"3","key":"9479_CR35","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/s11222-008-9056-0","volume":"18","author":"PD McNicholas","year":"2008","unstructured":"McNicholas, P. D., & Murphy, T. B. (2008). Parsimonious Gaussian mixture models. Statistics and Computing, 18(3), 285\u2013296.","journal-title":"Statistics and Computing"},{"issue":"21","key":"9479_CR36","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1093\/bioinformatics\/btq498","volume":"26","author":"PD McNicholas","year":"2010","unstructured":"McNicholas, P. D., & Murphy, T. B. (2010). Model-based clustering of microarray expression data via latent Gaussian mixture models. Bioinformatics, 26(21), 2705\u20132712.","journal-title":"Bioinformatics"},{"issue":"2","key":"9479_CR37","first-page":"7","volume":"1","author":"PD McNicholas","year":"2023","unstructured":"McNicholas, P. D., Murphy, T. B., ElSherbiny, A., Jampani, K. R., McDaid, A. F., & Banks, L. (2023). pgmm: Parsimonious gaussian mixture models. R Package Version, 1(2), 7.","journal-title":"R Package Version"},{"issue":"3","key":"9479_CR38","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.csda.2009.02.011","volume":"54","author":"PD McNicholas","year":"2010","unstructured":"McNicholas, P. D., Murphy, T. B., McDaid, A. F., & Frost, D. (2010). Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models. Computational Statistics and Data Analysis, 54(3), 711\u2013723.","journal-title":"Computational Statistics and Data Analysis"},{"key":"9479_CR39","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.csda.2014.03.012","volume":"77","author":"PM Murray","year":"2014","unstructured":"Murray, P. M., Browne, R. B., & McNicholas, P. D. (2014). Mixtures of skew-t factor analyzers. Computational Statistics and Data Analysis, 77, 326\u2013335.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"2","key":"9479_CR40","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1007\/s00357-019-9309-y","volume":"37","author":"PM Murray","year":"2020","unstructured":"Murray, P. M., Browne, R. P., & McNicholas, P. D. (2020). Mixtures of hidden truncation hyperbolic factor analyzers. Journal of Classification, 37(2), 366\u2013379.","journal-title":"Journal of Classification"},{"issue":"1","key":"9479_CR41","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1002\/sta4.43","volume":"3","author":"PM Murray","year":"2014","unstructured":"Murray, P. M., McNicholas, P. D., & Browne, R. B. (2014). A mixture of common skew-$$t$$ factor analyzers. Stat, 3(1), 68\u201382.","journal-title":"Stat"},{"key":"9479_CR42","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.csda.2014.09.006","volume":"93","author":"A O\u2019Hagan","year":"2016","unstructured":"O\u2019Hagan, A., Murphy, T. B., Gormley, I. C., McNicholas, P. D., & Karlis, D. (2016). Clustering with the multivariate normal inverse Gaussian distribution. Computational Statistics and Data Analysis, 93, 18\u201330.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"4","key":"9479_CR43","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1023\/A:1008981510081","volume":"10","author":"D Peel","year":"2000","unstructured":"Peel, D., & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339\u2013348.","journal-title":"Statistics and Computing"},{"issue":"6","key":"9479_CR44","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1002\/bimj.201500144","volume":"58","author":"A Punzo","year":"2024","unstructured":"Punzo, A., & McNicholas, P. D. (2024). Parsimonious mixtures of multivariate contaminated normal distributions. Biometrical Journal, 58(6), 1506\u20131537.","journal-title":"Biometrical Journal"},{"issue":"2","key":"9479_CR45","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"G Schwarz","year":"1978","unstructured":"Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461\u2013464.","journal-title":"The Annals of Statistics"},{"key":"9479_CR46","doi-asserted-by":"crossref","first-page":"387","DOI":"10.2307\/2529003","volume":"27","author":"AJ Scott","year":"1971","unstructured":"Scott, A. J., & Symons, M. J. (1971). Clustering methods based on likelihood ratio criteria. Biometrics, 27, 387\u2013397.","journal-title":"Biometrics"},{"key":"9479_CR47","doi-asserted-by":"crossref","unstructured":"Street, N.\u00a0W., Wolberg, W.\u00a0H., & Mangasarian, O.\u00a0L. (1993). Nuclear feature extraction for breast tumor diagnosis. In IS &T\/SPIE Symposium on Electronic Imaging: Science and Technology, (Volume 1905, pp. 861\u2014870), San Jose.","DOI":"10.1117\/12.148698"},{"issue":"1","key":"9479_CR48","doi-asserted-by":"crossref","first-page":"e177","DOI":"10.1002\/sta4.177","volume":"7","author":"Y Tang","year":"2018","unstructured":"Tang, Y., Browne, R. P., & McNicholas, P. D. (2018). Flexible clustering of high-dimensional data via mixtures of joint generalized hyperbolic distributions. Stat, 7(1), e177.","journal-title":"Stat"},{"key":"9479_CR49","volume-title":"Symposium on pattern analysis","author":"DV Tiedeman","year":"1955","unstructured":"Tiedeman, D. V. (1955). On the study of types. In S. B. Sells (Ed.), Symposium on pattern analysis. Randolph Field, Texas: Air University, U.S.A.F. School of Aviation Medicine."},{"key":"9479_CR50","doi-asserted-by":"crossref","unstructured":"Tomarchio, S.\u00a0D.,\u00a0Bagnato, L., &\u00a0Punzo, A. (2023). Model-based clustering using a new multivariate skew distribution. Advances in Data Analysis and Classification. In press.","DOI":"10.1007\/s11634-023-00552-8"},{"key":"9479_CR51","doi-asserted-by":"crossref","unstructured":"Tortora, C., Browne, R. P., ElSherbiny, A., Franczak, B. C., & McNicholas, P. D. (2021). Model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution: MixGHD R package. Journal of Statistical Software, 98, 3.","DOI":"10.18637\/jss.v098.i03"},{"issue":"4","key":"9479_CR52","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s11634-015-0204-z","volume":"10","author":"C Tortora","year":"2016","unstructured":"Tortora, C., McNicholas, P. D., & Browne, R. P. (2016). A mixture of generalized hyperbolic factor analyzers. Advances in Data Analysis and Classification, 10(4), 423\u2013440.","journal-title":"Advances in Data Analysis and Classification"},{"issue":"6","key":"9479_CR53","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.spl.2012.02.020","volume":"82","author":"I Vrbik","year":"2012","unstructured":"Vrbik, I., & McNicholas, P. D. (2012). Analytic calculations for the EM algorithm for multivariate skew-t mixture models. Statistics and Probability Letters, 82(6), 1169\u20131174.","journal-title":"Statistics and Probability Letters"},{"key":"9479_CR54","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.csda.2013.07.008","volume":"71","author":"I Vrbik","year":"2014","unstructured":"Vrbik, I., & McNicholas, P. D. (2014). Parsimonious skew mixture models for model-based clustering and classification. Computational Statistics and Data Analysis, 71, 196\u2013210.","journal-title":"Computational Statistics and Data Analysis"},{"key":"9479_CR55","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.csda.2018.08.016","volume":"130","author":"Y Wei","year":"2019","unstructured":"Wei, Y., Tang, Y., & McNicholas, P. D. (2019). Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data. Computational Statistics and Data Analysis, 130, 18\u201341.","journal-title":"Computational Statistics and Data Analysis"},{"issue":"3","key":"9479_CR56","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TPAMI.2018.2885760","volume":"42","author":"Y Wei","year":"2020","unstructured":"Wei, Y., Tang, Y., & McNicholas, P. D. (2020). Flexible high-dimensional unsupervised learning with missing data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(3), 610\u2013621.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"9479_CR57","doi-asserted-by":"crossref","unstructured":"Wolfe, J.\u00a0H. (1965). A computer program for the maximum likelihood analysis of types. Technical Bulletin 65-15, U.S. Naval Personnel Research Activity.","DOI":"10.21236\/AD0620026"}],"container-title":["Journal of Classification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-024-09479-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00357-024-09479-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00357-024-09479-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T08:10:33Z","timestamp":1742371833000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00357-024-09479-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,12]]},"references-count":57,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["9479"],"URL":"https:\/\/doi.org\/10.1007\/s00357-024-09479-x","relation":{},"ISSN":["0176-4268","1432-1343"],"issn-type":[{"type":"print","value":"0176-4268"},{"type":"electronic","value":"1432-1343"}],"subject":[],"published":{"date-parts":[[2024,7,12]]},"assertion":[{"value":"10 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}