Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions

Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in comparison to other mixtures of elliptical distributions, including mixtures of Gaussian distributions. A family of mixtures of multivariate ske...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Journal of classification Ročník 40; číslo 1; s. 145 - 167
Hlavní autori:	Dang, Utkarsh J., Gallaugher, Michael P.B., Browne, Ryan P., McNicholas, Paul D.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2023 Springer Nature B.V
Predmet:	Algorithms Bioinformatics Classification Cluster analysis Clustering Decomposition Eigenvalues Expected values Families & family life Kurtosis Manifolds (mathematics) Marketing Mathematics and Statistics Maximization Mixtures Multivariate analysis Normal distribution Normality Optimization Parameter estimation Pattern Recognition Power Psychometrics Random variables Search algorithms Signal,Image and Speech Processing Skewness Statistical Theory and Methods Statistics Model-based classification Multivariate skewed power exponential distribution Mixture models Model-based clustering Generalized expectation-maximization algorithm
ISSN:	0176-4268, 1432-1343
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in comparison to other mixtures of elliptical distributions, including mixtures of Gaussian distributions. A family of mixtures of multivariate skewed power exponential distributions is proposed that combines the flexibility of the MPE distribution with the ability to model skewness. These mixtures are more robust to variations from normality and can account for skewness, varying tail weight, and peakedness of data. A generalized expectation-maximization approach, which combines minorization-maximization and optimization based on accelerated line search algorithms on the Stiefel manifold, is used for parameter estimation. These mixtures are implemented both in the unsupervised and semi-supervised classification frameworks. Both simulated and real data are used for illustration and comparison to other mixture families.
AbstractList	Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in comparison to other mixtures of elliptical distributions, including mixtures of Gaussian distributions. A family of mixtures of multivariate skewed power exponential distributions is proposed that combines the flexibility of the MPE distribution with the ability to model skewness. These mixtures are more robust to variations from normality and can account for skewness, varying tail weight, and peakedness of data. A generalized expectation-maximization approach, which combines minorization-maximization and optimization based on accelerated line search algorithms on the Stiefel manifold, is used for parameter estimation. These mixtures are implemented both in the unsupervised and semi-supervised classification frameworks. Both simulated and real data are used for illustration and comparison to other mixture families.
Author	Browne, Ryan P. Dang, Utkarsh J. Gallaugher, Michael P.B. McNicholas, Paul D.
Author_xml	– sequence: 1 givenname: Utkarsh J. orcidid: 0000-0003-4120-2015 surname: Dang fullname: Dang, Utkarsh J. email: utkarshdang@cunet.carleton.ca organization: Department of Health Sciences, Carleton University – sequence: 2 givenname: Michael P.B. orcidid: 0000-0002-5487-8965 surname: Gallaugher fullname: Gallaugher, Michael P.B. organization: Department of Statistical Science, Baylor University – sequence: 3 givenname: Ryan P. orcidid: 0000-0003-4543-0218 surname: Browne fullname: Browne, Ryan P. organization: Department of Statistics & Actuarial Sciences, University of Waterloo – sequence: 4 givenname: Paul D. orcidid: 0000-0002-2482-523X surname: McNicholas fullname: McNicholas, Paul D. organization: Department of Mathematics & Statistics, McMaster University
BookMark	eNp9kMtOwzAQRS1UJNrCD7CKxNowfiROllDKQ2oFEnRtOYmDXEJcbIeWvydpkJBYdGWNPOfOzJmgUWMbjdA5gUsCIK48AIsFBkoxZJwKLI7QmHBGMWGcjdAYiEgwp0l6giber6GDkkSMkV3aUtf4RnldRrO69UE707xFqulL5b2pTKGCsU208v3H0uxC67SPbBUt2zqYL-WMCjp6edfbLuPZbrWL5rtNt2ATjKqjW-ODM3nbh_hTdFyp2uuz33eKVnfz19kDXjzdP86uF7hgJAs4S1mq04SWoDgvRMUFyQtKcgplJXhFhS6zmOdxSSAVOYgs1xwqpRKSZ5BlOZuiiyF34-xnq32Qa9u6phspGY15DGkSx11XOnQVznrvdCULE_bXBqdMLQnIXq8c9MpOr9zrlaJD6T9048yHct-HITZAftNb1u5vqwPUDyVKkJQ
CitedBy_id	crossref_primary_10_1080_00401706_2025_2467920 crossref_primary_10_3389_fgene_2023_1148147 crossref_primary_10_1080_01621459_2024_2427935 crossref_primary_10_1007_s00357_024_09479_x crossref_primary_10_1016_j_spl_2025_110507 crossref_primary_10_1002_bimj_70042
Cites_doi	10.1007/s00357-021-09401-9 10.1016/j.patcog.2018.02.025 10.1007/BF01908075 10.1016/j.jspi.2009.11.006 10.1016/j.jmva.2017.07.008 10.1007/s11634-014-0165-7 10.1007/s11222-015-9545-x 10.1111/biom.12351 10.1198/0003130042836 10.1007/s11222-013-9386-4 10.21236/AD0620026 10.1037/1082-989X.9.3.386 10.1007/s11222-011-9272-x 10.1007/s00357-018-9280-z 10.1002/prot.340110203 10.1016/j.csda.2012.12.008 10.1007/s11222-012-9362-4 10.1007/s11222-012-9364-2 10.1515/9781400830244 10.1093/biomet/83.4.715 10.1111/j.0006-341X.1999.01277.x 10.1198/016214504000000359 10.1002/cjs.11246 10.1111/j.2517-6161.1977.tb01600.x 10.1016/j.csda.2009.02.011 10.1007/s11222-008-9072-0 10.1016/S0888-7543(05)80111-9 10.1007/s11222-009-9128-9 10.1201/9781315373577 10.1162/089976699300016728 10.1007/BF00049423 10.1080/00401706.1966.10490375 10.2307/2532201 10.1016/j.stamet.2010.09.001 10.1016/S0167-9473(02)00163-9 10.1109/TPAMI.2013.216 10.1023/A:1008981510081 10.1016/j.csda.2014.03.012 10.1007/s11222-008-9056-0 10.1007/s00357-016-9211-9 10.1093/biomet/56.3.463 10.1109/ICIP.2008.4711718 10.1016/j.csda.2013.07.008 10.1007/s00357-015-9188-9 10.1007/978-3-319-41573-4_18 10.1007/978-0-387-21706-2 10.1214/cbms/1462106013 10.1214/aos/1176344136 10.1080/03610929808832115 10.1017/S0370164600022070 10.1006/jmva.2000.1960 10.1109/34.865189 10.1016/j.csda.2014.09.006 10.1016/j.spl.2013.04.011 10.1016/0031-3203(94)00125-6
ContentType	Journal Article
Copyright	The Author(s) under exclusive licence to The Classification Society 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s) under exclusive licence to The Classification Society 2023.
Copyright_xml	– notice: The Author(s) under exclusive licence to The Classification Society 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s) under exclusive licence to The Classification Society 2023.
DBID	AAYXX CITATION 3V. 7XB 88I 8AL 8FE 8FG 8FK 8G5 ABJCF ABUWG AFKRA ALSLI ARAPS AZQEC BENPR BGLVJ CCPQU CNYFK DWQXO E3H F2A GNUQQ GUQSH HCIFZ JQ2 K7- L6V M0N M1O M2O M2P M7S MBDVC P5Z P62 PADUT PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PRQQA PTHSS Q9U
DOI	10.1007/s00357-022-09427-7
DatabaseName	CrossRef ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Social Science Premium Collection Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Social Science Premium Collection - Library & Information Science Collection ProQuest Central Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Engineering Collection Computing Database Library Science Database (ProQuest) Research Library Science Database Engineering Database Research Library (Corporate) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Research Library China Proquest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Social Sciences Engineering Collection ProQuest Central Basic
DatabaseTitle	CrossRef Research Library Prep Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) Library and Information Science Abstracts (LISA) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Library Science ProQuest Engineering Collection ProQuest Central Korea Library & Information Science Collection ProQuest Research Library Research Library China ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Social Science Premium Collection ProQuest Computing Engineering Database ProQuest Science Journals (Alumni Edition) ProQuest One Social Sciences ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni)
DatabaseTitleList	Research Library Prep
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Mathematics Library & Information Science Statistics Psychology Philosophy
EISSN	1432-1343
EndPage	167
ExternalDocumentID	10_1007_s00357_022_09427_7
GrantInformation_xml	– fundername: Natural Sciences and Engineering Research Council of Canada grantid: Vanier Canada Graduate Scholarship funderid: https://doi.org/10.13039/501100000038 – fundername: Canada Research Chairs funderid: https://doi.org/10.13039/501100001804 – fundername: Natural Sciences and Engineering Research Council of Canada funderid: https://doi.org/10.13039/501100000038
GroupedDBID	-52 -5D -5G -BR -EM -Y2 -~C .4I .86 .VR 06D 0R~ 0VY 199 1N0 1SB 203 28- 29K 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 53G 5GY 5QI 5VS 67Z 6NX 77K 78A 88I 8FE 8FG 8G5 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHQT ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALSLI ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. B0M BA0 BAPOH BBWZM BDATZ BENPR BGLVJ BGNMA BPHCQ BSONS CAG CCPQU CNYFK COF CS3 CSCUP D-I DDRTE DL5 DNIVK DPUIP DU5 DWQXO EAD EAP EBD EBLON EBS EIOEI EJD ELW EMK EPL ESBYG EST ESX FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GUQSH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV KOW L6V LAS LLZTM M0N M1O M2O M2P M4Y M7S MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9R PADUT PF0 PQQKQ PROAC PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RIG RNI ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCLPG SDD SDH SDM SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WJK YLTOR Z45 Z81 Z83 Z8U Z8W Z92 ZMTXR ZWQNP ~8M ~A9 ~EX 77I AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB PRQQA 7XB 8AL 8FK E3H F2A JQ2 MBDVC PKEHL PQEST PQUKI PRINS Q9U
ID	FETCH-LOGICAL-c319t-9838e862d0a44c7f471bc21b20df74f27ed954b5d1087b079be40faa61b9099b3
IEDL.DBID	RSV
ISICitedReferencesCount	8
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000933353600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0176-4268
IngestDate	Sat Nov 15 05:52:40 EST 2025 Sat Nov 29 02:13:06 EST 2025 Tue Nov 18 22:33:10 EST 2025 Fri Feb 21 02:45:09 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Model-based classification Multivariate skewed power exponential distribution Mixture models Model-based clustering Generalized expectation-maximization algorithm
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c319t-9838e862d0a44c7f471bc21b20df74f27ed954b5d1087b079be40faa61b9099b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-4543-0218 0000-0002-2482-523X 0000-0002-5487-8965 0000-0003-4120-2015
PQID	3254508655
PQPubID	54109
PageCount	23
ParticipantIDs	proquest_journals_3254508655 crossref_citationtrail_10_1007_s00357_022_09427_7 crossref_primary_10_1007_s00357_022_09427_7 springer_journals_10_1007_s00357_022_09427_7
PublicationCentury	2000
PublicationDate	2023-04-01
PublicationDateYYYYMMDD	2023-04-01
PublicationDate_xml	– month: 04 year: 2023 text: 2023-04-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	Journal of classification
PublicationTitleAbbrev	J Classif
PublicationYear	2023
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	McNicholasPDMurphyTBMcDaidAFFrostDSerial and parallel implementations of model-based clustering via parsimonious gaussian mixture modelsComputational Statistics & Data Analysis201054371172327444271464.6213110.1016/j.csda.2009.02.011 VrbikIMcNicholasPDFractionally-supervised classificationJournal of Classification201532335938134228921331.6231910.1007/s00357-015-9188-9 Flury, B. (2012). Flury: data sets from Flury, 1997. R package version 0.1–3. DayNEEstimating the components of a mixture of normal distributionsBiometrika19695634634742549560183.4810610.1093/biomet/56.3.463 LeeSXMcLachlanGJFinite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture modelsStatistics and Computing201626357358934898581420.6002010.1007/s11222-015-9545-x McNicholasPDMurphyTBParsimonious gaussian mixture modelsStatistics and Computing2008183285296241338510.1007/s11222-008-9056-0 HunterDRLangeKRejoinder to discussion of optimization transfer using surrogate objective functionsJournal of Computational and Graphical Statistics2000915259 BrowneRPMcNicholasPDOrthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture modelsStatistics and Computing201424220321031655481325.6200810.1007/s11222-012-9364-2 ChoDBuiTDMultivariate statistical modeling for image denoising using wavelet transformsSignal Processing: Image Communication20052017789 VenablesWNRipleyBDModern applied statistics with S, fourth edn.2002New YorkSpringer1006.6200310.1007/978-0-387-21706-2ISBN 0-387-95457-0 NakaiKKanehisaMA knowledge base for predicting protein localization sites in eukaryotic cellsGenomics199214489791110.1016/S0888-7543(05)80111-9 KarlisDSantourianAModel-based clustering with non-elliptically contoured distributionsStatistics and Computing20091917383247055910.1007/s11222-008-9072-0 Dang, U.J., Browne, R.P., Gallaugher, M.P., & Band McNicholas, P.D. (2021). mixSPE: Mixtures of power exponential and skew power exponential distributions for use in model-based clustering and classification. R package version 0.9.1. Wolfe, J.H. (1965). A computer program for the maximum likelihood analysis of types. U.S. Naval personnel research activity, technical bulletin:65-15. HubertLArabiePComparing partitionsJournal of Classification1985211932180587.6212810.1007/BF01908075 NakaiKKanehisaMExpert system for predicting protein localization sites in gram-negative bacteria. Proteins: StructureFunction, and Bioinformatics19911129511010.1002/prot.340110203 FranczakBCBrowneRPMcNicholasPDBurakKLMixSAL: Mixtures of multivariate shifted asymmetric Laplace (SAL) distributionsR package version201810 SchwarzGEstimating the dimension of a modelThe Annals of Statistics1978624614644680140379.6200510.1214/aos/1176344136 MorrisKMcNicholasPDDimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributionsStatistics and Probability Letters20138392088209330790501282.6215310.1016/j.spl.2013.04.011 GallaugherMPBMcNicholasPDOn fractionally-supervised classification: Weight selection and extension to the multivariate t-distributionJournal of Classification201936223226540263991436.6225210.1007/s00357-018-9280-z Fraley, C., Raftery, A. E., Murphy, T. B., & Scrucca, L. (2012). mclust version 4 for R: Normal mixture modeling for model-based clustering, classification, and density estimation. Technical report 597, Department of statistics, university of Washington, Seattle, Washington. SubediSMcNicholasPDVariational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributionsAdvances in Data Analysis and Classification20148216719332102651459.6212210.1007/s11634-014-0165-7 MurrayPMBrowneRBMcNicholasPDHidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clusteringJournal of Multivariate Analysis201716114115636981201403.6202810.1016/j.jmva.2017.07.008 Verdoolaege, G., De Backer, S., & Scheunders, P. (2008). Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models. In 2008 15th IEEE international conference on image processing (pp. 169–172). BöhningDLindsayBGMonotonicity of quadratic-approximation algorithmsAnnals of the Institute of Statistical Mathematics19884046416639966900723.6515010.1007/BF00049423 DiCiccioTJMontiACInferential aspects of the skew exponential power distributionJournal of the American Statistical Association20049946643945020628291117.6231810.1198/016214504000000359 LindseyJKMultivariate elliptically contoured distributions for repeated measurementsBiometrics1999554127712801059.6254310.1111/j.0006-341X.1999.01277.x Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. In NSF-CBMS regional conference series in probability and statistics (pp. 1–163). MurrayPMBrowneRBMcNicholasPDMixtures of skew-t factor analyzersComputational Statistics and Data Analysis20147732633532100661506.6213210.1016/j.csda.2014.03.012 AzzaliniAValleADThe multivariate skew-normal distributionBiometrika19968371572614400390885.6206210.1093/biomet/83.4.715 BrancoMDDeyDKA general class of multivariate skew-elliptical distributionsJournal of Multivariate Analysis20017919911318672570992.6204710.1006/jmva.2000.1960 BasfordKGreenwayDMcLachlanGPeelDStandard errors of fitted component means of normal mixturesComputational Statistics19971211180924.62055 BiernackiCCeleuxGGovaertGAssessing a mixture model for clustering with the integrated completed likelihoodIEEE Transactions on Pattern Analysis and Machine Intelligence200022771972510.1109/34.865189 Hurley, C. (2012). gclus: clustering Graphics. R package version 1.3.1. CeleuxGGovaertGGaussian parsimonious clustering modelsPattern Recognition199528578179310.1016/0031-3203(94)00125-6 Horst, A.M., Hill, A.P., & Gorman, K.B. (2020). Palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. ZhuXSarkarSMelnykovVMattransmix: An r package for matrix model-based clustering and parsimonious mixture modelingJournal of Classification202239114717043948310751235710.1007/s00357-021-09401-9 Pocuca, N., Browne, R.P., & McNicholas, P.D. (2022). Mixture: Mixture models for clustering and classification. R package version 2.0.5. BouveyronCBrunet-SaumardCModel-based clustering of high-dimensional data: A reviewComputational Statistics and Data Analysis201471527831319541471.6203210.1016/j.csda.2012.12.008 DempsterAPLairdNMRubinDBMaximum likelihood from incomplete data via the EM algorithmJournal of the Royal Statistical Society. Series B19773911385015370364.62022 HasselbladVEstimation of parameters for a mixture of normal distributionsTechnometrics19668343144419684210.1080/00401706.1966.10490375 BrowneRPMcNicholasPDA mixture of generalized hyperbolic distributionsCanadian Journal of Statistics201543217619833533791320.6214410.1002/cjs.11246 DangUJBrowneRPMcNicholasPDMixtures of multivariate power exponential distributionsBiometrics20157141081108934367331419.6233010.1111/biom.12351 PeelDMcLachlanGJRobust mixture modelling using the t distributionStatistics and Computing200010433934810.1023/A:1008981510081 SteinleyDProperties of the Hubert-Arabie adjusted Rand indexPsychological Methods2004938639610.1037/1082-989X.9.3.386 BiernackiCCeleuxGGovaertGChoosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture modelsComputational Statistics & Data Analysis2003413–456157519680691429.6223510.1016/S0167-9473(02)00163-9 LinT-IRobust mixture modeling using multivariate skew t distributionsStatistics and Computing2010203343356272539210.1007/s11222-009-9128-9 ForinaMTiscorniaEPattern-recognition methods in the prediction of italian olive oil origin by their fatty-acid contentAnnali di Chimica1982723-4143155 da Silva FerreiraCBolfarineHLachosVHSkew scale mixtures of normal distributions: Properties and estimationStatistical Methodology20118215417127692771213.6202310.1016/j.stamet.2010.09.001 Absil, P.-A., Mahony, R., & Sepulchre, R. (2009). Optimization algorithms on matrix manifolds. Princeton University Press. HartiganJAWongMAA k-means clustering algorithmJournal of the Royal Statistical Society: Series C19792811001080447.62062 Aitken, A.C. (1926). On Bernoulli’s numerical solution of algebraic equations. In Proceedings of the royal society of edinburgh (pp. 289–305). LeeSMcLachlanGJFinite mixtures of multivariate skew t-distributions: some recent and new resultsStatistics and Computing201424218120231655471325.6210710.1007/s11222-012-9362-4 HunterDRLangeKA tutorial on MM algorithmsThe American Statistician20045813037205550910.1198/0003130042836 AndrewsJLMcNicholasPDModel-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributionsStatistics and Computing20122251021102929500821252.6206210.1007/s11222-011-9272-x VrbikIMcNicholasPDParsimonious skew mixture models for model-based clustering and classificationComputational Statistics and Data Analysis20147119621031319641471.6220210.1016/j.csda.2013.07.008 GallaugherMPBMcNicholasPDFinite mixtures of skewed matrix variate distributionsPattern Recognition201880839310.1016/j.patcog.2018.02.025 McNicholas, S.M., McNicholas, P.D., & Browne, R.P. (2017). A mixture of variance-gamma factor analyzers. In Big and complex data analysis (pp 369–385). Springer international publishing, Cham. McNicholas, P.D., ElSherbiny, A., McDaid, A.F., & Murphy, T. B. (2022). pgmm: Parsimonious Gaussian Mixture Models. R package version 1.2.6-2. Tortora, C., ElSherbiny, A., Browne, R. P., Franczak, B.C., McNicholas, P.D., & Amos, D.D. (2018). MixGHD: Model based clustering, classification and discriminant analysis using the mixture of generalized hyperbolic distributions. R package version 2.2. TippingMEBishopCMMixtures of probabilistic principal component analysersNeural Computation199911244348210.1162/089976699300016728 O’HaganAMurphyTBGormleyICMcNicholasPDKarlisDClustering with the multivariate normal inverse gaussian distributionComput JL Andrews (9427_CR3) 2012; 22 ME Tipping (9427_CR64) 1999; 11 PD McNicholas (9427_CR45) 2010; 140 9427_CR23 9427_CR67 MPB Gallaugher (9427_CR29) 2019; 36 9427_CR65 9427_CR62 A Azzalini (9427_CR5) 1996; 83 JA Hartigan (9427_CR31) 1979; 28 X Zhu (9427_CR71) 2022; 39 D Böhning (9427_CR10) 1988; 40 9427_CR25 WN Venables (9427_CR66) 2002 D Cho (9427_CR16) 2005; 20 K Morris (9427_CR52) 2013; 83 UJ Dang (9427_CR18) 2015; 71 PM Murray (9427_CR54) 2017; 161 I Vrbik (9427_CR68) 2014; 71 T-I Lin (9427_CR41) 2010; 20 PM Murray (9427_CR53) 2014; 77 A O’Hagan (9427_CR57) 2016; 93 K Nakai (9427_CR55) 1991; 11 D Peel (9427_CR58) 2000; 10 BC Franczak (9427_CR27) 2018; 1 9427_CR51 9427_CR50 9427_CR19 DR Hunter (9427_CR35) 2000; 9 MD Branco (9427_CR12) 2001; 79 E Gómez (9427_CR30) 1998; 27 9427_CR59 PD McNicholas (9427_CR48) 2008; 18 T-I Lin (9427_CR42) 2014; 24 K Basford (9427_CR7) 1997; 12 G Schwarz (9427_CR60) 1978; 6 V Hasselblad (9427_CR32) 1966; 8 MPB Gallaugher (9427_CR28) 2018; 80 C Biernacki (9427_CR9) 2003; 41 D Steinley (9427_CR61) 2004; 9 L Hubert (9427_CR34) 1985; 2 PD McNicholas (9427_CR46) 2016 SX Lee (9427_CR40) 2016; 26 9427_CR43 PD McNicholas (9427_CR49) 2010; 54 BC Franczak (9427_CR26) 2014; 36 I Vrbik (9427_CR69) 2015; 32 JD Banfield (9427_CR6) 1993; 49 NE Day (9427_CR20) 1969; 56 AP Dempster (9427_CR21) 1977; 39 S Lee (9427_CR39) 2014; 24 A Azzalini (9427_CR4) 1986; 46 D Karlis (9427_CR38) 2009; 19 C da Silva Ferreira (9427_CR17) 2011; 8 9427_CR70 M Forina (9427_CR24) 1982; 72 9427_CR2 9427_CR33 DR Hunter (9427_CR36) 2004; 58 RP Browne (9427_CR13) 2014; 24 TJ DiCiccio (9427_CR22) 2004; 99 K Nakai (9427_CR56) 1992; 14 C Bouveyron (9427_CR11) 2014; 71 9427_CR1 G Celeux (9427_CR15) 1995; 28 9427_CR37 C Biernacki (9427_CR8) 2000; 22 JK Lindsey (9427_CR44) 1999; 55 RP Browne (9427_CR14) 2015; 43 PD McNicholas (9427_CR47) 2016; 33 S Subedi (9427_CR63) 2014; 8
References_xml	– reference: McNicholasPDMurphyTBParsimonious gaussian mixture modelsStatistics and Computing2008183285296241338510.1007/s11222-008-9056-0 – reference: TippingMEBishopCMMixtures of probabilistic principal component analysersNeural Computation199911244348210.1162/089976699300016728 – reference: Pocuca, N., Browne, R.P., & McNicholas, P.D. (2022). Mixture: Mixture models for clustering and classification. R package version 2.0.5. – reference: SteinleyDProperties of the Hubert-Arabie adjusted Rand indexPsychological Methods2004938639610.1037/1082-989X.9.3.386 – reference: BiernackiCCeleuxGGovaertGAssessing a mixture model for clustering with the integrated completed likelihoodIEEE Transactions on Pattern Analysis and Machine Intelligence200022771972510.1109/34.865189 – reference: NakaiKKanehisaMExpert system for predicting protein localization sites in gram-negative bacteria. Proteins: StructureFunction, and Bioinformatics19911129511010.1002/prot.340110203 – reference: Streuli, H. (1973). Der heutige stand der kaffeechemie. In 6th international colloquium on coffee chemisrty (pp. 61–72). – reference: Hurley, C. (2012). gclus: clustering Graphics. R package version 1.3.1. – reference: SchwarzGEstimating the dimension of a modelThe Annals of Statistics1978624614644680140379.6200510.1214/aos/1176344136 – reference: DempsterAPLairdNMRubinDBMaximum likelihood from incomplete data via the EM algorithmJournal of the Royal Statistical Society. Series B19773911385015370364.62022 – reference: LeeSXMcLachlanGJFinite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture modelsStatistics and Computing201626357358934898581420.6002010.1007/s11222-015-9545-x – reference: McNicholasPDMixture model-based classification2016Boca RatonChapman & Hall/CRC Press1454.6200510.1201/9781315373577 – reference: HartiganJAWongMAA k-means clustering algorithmJournal of the Royal Statistical Society: Series C19792811001080447.62062 – reference: LinT-IRobust mixture modeling using multivariate skew t distributionsStatistics and Computing2010203343356272539210.1007/s11222-009-9128-9 – reference: BöhningDLindsayBGMonotonicity of quadratic-approximation algorithmsAnnals of the Institute of Statistical Mathematics19884046416639966900723.6515010.1007/BF00049423 – reference: McNicholasPDMurphyTBMcDaidAFFrostDSerial and parallel implementations of model-based clustering via parsimonious gaussian mixture modelsComputational Statistics & Data Analysis201054371172327444271464.6213110.1016/j.csda.2009.02.011 – reference: HubertLArabiePComparing partitionsJournal of Classification1985211932180587.6212810.1007/BF01908075 – reference: Flury, B. (2012). Flury: data sets from Flury, 1997. R package version 0.1–3. – reference: AndrewsJLMcNicholasPDModel-based clustering, classification, and discriminant analysis via mixtures of multivariate t-distributionsStatistics and Computing20122251021102929500821252.6206210.1007/s11222-011-9272-x – reference: BouveyronCBrunet-SaumardCModel-based clustering of high-dimensional data: A reviewComputational Statistics and Data Analysis201471527831319541471.6203210.1016/j.csda.2012.12.008 – reference: Dang, U.J., Browne, R.P., Gallaugher, M.P., & Band McNicholas, P.D. (2021). mixSPE: Mixtures of power exponential and skew power exponential distributions for use in model-based clustering and classification. R package version 0.9.1. – reference: LinT-IHoHJLeeC-RFlexible mixture modelling using the multivariate skew-t-normal distributionStatistics and Computing201424453154632235391325.6211310.1007/s11222-013-9386-4 – reference: McNicholasPDModel-based classification using latent Gaussian mixture modelsJournal of Statistical Planning and Inference201014051175118125811201181.6209510.1016/j.jspi.2009.11.006 – reference: KarlisDSantourianAModel-based clustering with non-elliptically contoured distributionsStatistics and Computing20091917383247055910.1007/s11222-008-9072-0 – reference: GallaugherMPBMcNicholasPDOn fractionally-supervised classification: Weight selection and extension to the multivariate t-distributionJournal of Classification201936223226540263991436.6225210.1007/s00357-018-9280-z – reference: AzzaliniAFurther results on a class of distributions which includes the normal onesStatistica19864621992088777200606.62013 – reference: BanfieldJDRafteryAEModel-based Gaussian and non-Gaussian clusteringBiometrics199349380382112434940794.6203410.2307/2532201 – reference: BrowneRPMcNicholasPDOrthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture modelsStatistics and Computing201424220321031655481325.6200810.1007/s11222-012-9364-2 – reference: SubediSMcNicholasPDVariational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributionsAdvances in Data Analysis and Classification20148216719332102651459.6212210.1007/s11634-014-0165-7 – reference: FranczakBCBrowneRPMcNicholasPDMixtures of shifted asymmetric Laplace distributionsIEEE Transactions on Pattern Analysis and Machine Intelligence20143661149115710.1109/TPAMI.2013.216 – reference: Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. In NSF-CBMS regional conference series in probability and statistics (pp. 1–163). – reference: Verdoolaege, G., De Backer, S., & Scheunders, P. (2008). Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models. In 2008 15th IEEE international conference on image processing (pp. 169–172). – reference: Aitken, A.C. (1926). On Bernoulli’s numerical solution of algebraic equations. In Proceedings of the royal society of edinburgh (pp. 289–305). – reference: MurrayPMBrowneRBMcNicholasPDMixtures of skew-t factor analyzersComputational Statistics and Data Analysis20147732633532100661506.6213210.1016/j.csda.2014.03.012 – reference: Tortora, C., ElSherbiny, A., Browne, R. P., Franczak, B.C., McNicholas, P.D., & Amos, D.D. (2018). MixGHD: Model based clustering, classification and discriminant analysis using the mixture of generalized hyperbolic distributions. R package version 2.2. – reference: CeleuxGGovaertGGaussian parsimonious clustering modelsPattern Recognition199528578179310.1016/0031-3203(94)00125-6 – reference: O’HaganAMurphyTBGormleyICMcNicholasPDKarlisDClustering with the multivariate normal inverse gaussian distributionComputational Statistics and Data Analysis201693183034061931468.6215110.1016/j.csda.2014.09.006 – reference: BasfordKGreenwayDMcLachlanGPeelDStandard errors of fitted component means of normal mixturesComputational Statistics19971211180924.62055 – reference: LeeSMcLachlanGJFinite mixtures of multivariate skew t-distributions: some recent and new resultsStatistics and Computing201424218120231655471325.6210710.1007/s11222-012-9362-4 – reference: ForinaMTiscorniaEPattern-recognition methods in the prediction of italian olive oil origin by their fatty-acid contentAnnali di Chimica1982723-4143155 – reference: FranczakBCBrowneRPMcNicholasPDBurakKLMixSAL: Mixtures of multivariate shifted asymmetric Laplace (SAL) distributionsR package version201810 – reference: Fraley, C., Raftery, A. E., Murphy, T. B., & Scrucca, L. (2012). mclust version 4 for R: Normal mixture modeling for model-based clustering, classification, and density estimation. Technical report 597, Department of statistics, university of Washington, Seattle, Washington. – reference: HunterDRLangeKA tutorial on MM algorithmsThe American Statistician20045813037205550910.1198/0003130042836 – reference: VenablesWNRipleyBDModern applied statistics with S, fourth edn.2002New YorkSpringer1006.6200310.1007/978-0-387-21706-2ISBN 0-387-95457-0 – reference: BiernackiCCeleuxGGovaertGChoosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture modelsComputational Statistics & Data Analysis2003413–456157519680691429.6223510.1016/S0167-9473(02)00163-9 – reference: GallaugherMPBMcNicholasPDFinite mixtures of skewed matrix variate distributionsPattern Recognition201880839310.1016/j.patcog.2018.02.025 – reference: ZhuXSarkarSMelnykovVMattransmix: An r package for matrix model-based clustering and parsimonious mixture modelingJournal of Classification202239114717043948310751235710.1007/s00357-021-09401-9 – reference: McNicholas, P.D., ElSherbiny, A., McDaid, A.F., & Murphy, T. B. (2022). pgmm: Parsimonious Gaussian Mixture Models. R package version 1.2.6-2. – reference: VrbikIMcNicholasPDParsimonious skew mixture models for model-based clustering and classificationComputational Statistics and Data Analysis20147119621031319641471.6220210.1016/j.csda.2013.07.008 – reference: MurrayPMBrowneRBMcNicholasPDHidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clusteringJournal of Multivariate Analysis201716114115636981201403.6202810.1016/j.jmva.2017.07.008 – reference: HasselbladVEstimation of parameters for a mixture of normal distributionsTechnometrics19668343144419684210.1080/00401706.1966.10490375 – reference: Horst, A.M., Hill, A.P., & Gorman, K.B. (2020). Palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. – reference: PeelDMcLachlanGJRobust mixture modelling using the t distributionStatistics and Computing200010433934810.1023/A:1008981510081 – reference: GómezEGomez-ViilegasMAMarinJMA multivariate generalization of the power exponential family of distributionsCommunications in Statistics-Theory and Methods199827358960016190300895.6205310.1080/03610929808832115 – reference: BrancoMDDeyDKA general class of multivariate skew-elliptical distributionsJournal of Multivariate Analysis20017919911318672570992.6204710.1006/jmva.2000.1960 – reference: AzzaliniAValleADThe multivariate skew-normal distributionBiometrika19968371572614400390885.6206210.1093/biomet/83.4.715 – reference: DiCiccioTJMontiACInferential aspects of the skew exponential power distributionJournal of the American Statistical Association20049946643945020628291117.6231810.1198/016214504000000359 – reference: Wolfe, J.H. (1965). A computer program for the maximum likelihood analysis of types. U.S. Naval personnel research activity, technical bulletin:65-15. – reference: BrowneRPMcNicholasPDA mixture of generalized hyperbolic distributionsCanadian Journal of Statistics201543217619833533791320.6214410.1002/cjs.11246 – reference: McNicholas, S.M., McNicholas, P.D., & Browne, R.P. (2017). A mixture of variance-gamma factor analyzers. In Big and complex data analysis (pp 369–385). Springer international publishing, Cham. – reference: Absil, P.-A., Mahony, R., & Sepulchre, R. (2009). Optimization algorithms on matrix manifolds. Princeton University Press. – reference: NakaiKKanehisaMA knowledge base for predicting protein localization sites in eukaryotic cellsGenomics199214489791110.1016/S0888-7543(05)80111-9 – reference: ChoDBuiTDMultivariate statistical modeling for image denoising using wavelet transformsSignal Processing: Image Communication20052017789 – reference: LindseyJKMultivariate elliptically contoured distributions for repeated measurementsBiometrics1999554127712801059.6254310.1111/j.0006-341X.1999.01277.x – reference: VrbikIMcNicholasPDFractionally-supervised classificationJournal of Classification201532335938134228921331.6231910.1007/s00357-015-9188-9 – reference: McNicholasPDModel-based clusteringJournal of Classification201633333137335756211364.6215510.1007/s00357-016-9211-9 – reference: DangUJBrowneRPMcNicholasPDMixtures of multivariate power exponential distributionsBiometrics20157141081108934367331419.6233010.1111/biom.12351 – reference: da Silva FerreiraCBolfarineHLachosVHSkew scale mixtures of normal distributions: Properties and estimationStatistical Methodology20118215417127692771213.6202310.1016/j.stamet.2010.09.001 – reference: DayNEEstimating the components of a mixture of normal distributionsBiometrika19695634634742549560183.4810610.1093/biomet/56.3.463 – reference: HunterDRLangeKRejoinder to discussion of optimization transfer using surrogate objective functionsJournal of Computational and Graphical Statistics2000915259 – reference: MorrisKMcNicholasPDDimension reduction for model-based clustering via mixtures of shifted asymmetric Laplace distributionsStatistics and Probability Letters20138392088209330790501282.6215310.1016/j.spl.2013.04.011 – volume: 39 start-page: 147 issue: 1 year: 2022 ident: 9427_CR71 publication-title: Journal of Classification doi: 10.1007/s00357-021-09401-9 – ident: 9427_CR19 – volume: 80 start-page: 83 year: 2018 ident: 9427_CR28 publication-title: Pattern Recognition doi: 10.1016/j.patcog.2018.02.025 – volume: 72 start-page: 143 issue: 3-4 year: 1982 ident: 9427_CR24 publication-title: Annali di Chimica – volume: 2 start-page: 193 issue: 1 year: 1985 ident: 9427_CR34 publication-title: Journal of Classification doi: 10.1007/BF01908075 – volume: 140 start-page: 1175 issue: 5 year: 2010 ident: 9427_CR45 publication-title: Journal of Statistical Planning and Inference doi: 10.1016/j.jspi.2009.11.006 – volume: 161 start-page: 141 year: 2017 ident: 9427_CR54 publication-title: Journal of Multivariate Analysis doi: 10.1016/j.jmva.2017.07.008 – ident: 9427_CR25 – volume: 8 start-page: 167 issue: 2 year: 2014 ident: 9427_CR63 publication-title: Advances in Data Analysis and Classification doi: 10.1007/s11634-014-0165-7 – volume: 26 start-page: 573 issue: 3 year: 2016 ident: 9427_CR40 publication-title: Statistics and Computing doi: 10.1007/s11222-015-9545-x – volume: 71 start-page: 1081 issue: 4 year: 2015 ident: 9427_CR18 publication-title: Biometrics doi: 10.1111/biom.12351 – volume: 58 start-page: 30 issue: 1 year: 2004 ident: 9427_CR36 publication-title: The American Statistician doi: 10.1198/0003130042836 – volume: 24 start-page: 531 issue: 4 year: 2014 ident: 9427_CR42 publication-title: Statistics and Computing doi: 10.1007/s11222-013-9386-4 – ident: 9427_CR70 doi: 10.21236/AD0620026 – volume: 9 start-page: 386 year: 2004 ident: 9427_CR61 publication-title: Psychological Methods doi: 10.1037/1082-989X.9.3.386 – ident: 9427_CR50 – volume: 22 start-page: 1021 issue: 5 year: 2012 ident: 9427_CR3 publication-title: Statistics and Computing doi: 10.1007/s11222-011-9272-x – volume: 36 start-page: 232 issue: 2 year: 2019 ident: 9427_CR29 publication-title: Journal of Classification doi: 10.1007/s00357-018-9280-z – volume: 11 start-page: 95 issue: 2 year: 1991 ident: 9427_CR55 publication-title: Function, and Bioinformatics doi: 10.1002/prot.340110203 – volume: 71 start-page: 52 year: 2014 ident: 9427_CR11 publication-title: Computational Statistics and Data Analysis doi: 10.1016/j.csda.2012.12.008 – volume: 24 start-page: 181 issue: 2 year: 2014 ident: 9427_CR39 publication-title: Statistics and Computing doi: 10.1007/s11222-012-9362-4 – volume: 20 start-page: 77 issue: 1 year: 2005 ident: 9427_CR16 publication-title: Signal Processing: Image Communication – volume: 24 start-page: 203 issue: 2 year: 2014 ident: 9427_CR13 publication-title: Statistics and Computing doi: 10.1007/s11222-012-9364-2 – ident: 9427_CR1 doi: 10.1515/9781400830244 – volume: 83 start-page: 715 year: 1996 ident: 9427_CR5 publication-title: Biometrika doi: 10.1093/biomet/83.4.715 – volume: 55 start-page: 1277 issue: 4 year: 1999 ident: 9427_CR44 publication-title: Biometrics doi: 10.1111/j.0006-341X.1999.01277.x – volume: 99 start-page: 439 issue: 466 year: 2004 ident: 9427_CR22 publication-title: Journal of the American Statistical Association doi: 10.1198/016214504000000359 – volume: 43 start-page: 176 issue: 2 year: 2015 ident: 9427_CR14 publication-title: Canadian Journal of Statistics doi: 10.1002/cjs.11246 – volume: 39 start-page: 1 issue: 1 year: 1977 ident: 9427_CR21 publication-title: Journal of the Royal Statistical Society. Series B doi: 10.1111/j.2517-6161.1977.tb01600.x – volume: 54 start-page: 711 issue: 3 year: 2010 ident: 9427_CR49 publication-title: Computational Statistics & Data Analysis doi: 10.1016/j.csda.2009.02.011 – volume: 19 start-page: 73 issue: 1 year: 2009 ident: 9427_CR38 publication-title: Statistics and Computing doi: 10.1007/s11222-008-9072-0 – volume: 14 start-page: 897 issue: 4 year: 1992 ident: 9427_CR56 publication-title: Genomics doi: 10.1016/S0888-7543(05)80111-9 – volume: 20 start-page: 343 issue: 3 year: 2010 ident: 9427_CR41 publication-title: Statistics and Computing doi: 10.1007/s11222-009-9128-9 – volume: 46 start-page: 199 issue: 2 year: 1986 ident: 9427_CR4 publication-title: Statistica – volume-title: Mixture model-based classification year: 2016 ident: 9427_CR46 doi: 10.1201/9781315373577 – volume: 9 start-page: 52 issue: 1 year: 2000 ident: 9427_CR35 publication-title: Journal of Computational and Graphical Statistics – volume: 11 start-page: 443 issue: 2 year: 1999 ident: 9427_CR64 publication-title: Neural Computation doi: 10.1162/089976699300016728 – volume: 40 start-page: 641 issue: 4 year: 1988 ident: 9427_CR10 publication-title: Annals of the Institute of Statistical Mathematics doi: 10.1007/BF00049423 – ident: 9427_CR65 – ident: 9427_CR23 – volume: 8 start-page: 431 issue: 3 year: 1966 ident: 9427_CR32 publication-title: Technometrics doi: 10.1080/00401706.1966.10490375 – volume: 49 start-page: 803 issue: 3 year: 1993 ident: 9427_CR6 publication-title: Biometrics doi: 10.2307/2532201 – volume: 28 start-page: 100 issue: 1 year: 1979 ident: 9427_CR31 publication-title: Journal of the Royal Statistical Society: Series C – volume: 8 start-page: 154 issue: 2 year: 2011 ident: 9427_CR17 publication-title: Statistical Methodology doi: 10.1016/j.stamet.2010.09.001 – volume: 41 start-page: 561 issue: 3–4 year: 2003 ident: 9427_CR9 publication-title: Computational Statistics & Data Analysis doi: 10.1016/S0167-9473(02)00163-9 – ident: 9427_CR37 – volume: 36 start-page: 1149 issue: 6 year: 2014 ident: 9427_CR26 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2013.216 – volume: 10 start-page: 339 issue: 4 year: 2000 ident: 9427_CR58 publication-title: Statistics and Computing doi: 10.1023/A:1008981510081 – ident: 9427_CR33 – volume: 77 start-page: 326 year: 2014 ident: 9427_CR53 publication-title: Computational Statistics and Data Analysis doi: 10.1016/j.csda.2014.03.012 – volume: 12 start-page: 1 issue: 1 year: 1997 ident: 9427_CR7 publication-title: Computational Statistics – volume: 18 start-page: 285 issue: 3 year: 2008 ident: 9427_CR48 publication-title: Statistics and Computing doi: 10.1007/s11222-008-9056-0 – volume: 1 start-page: 0 year: 2018 ident: 9427_CR27 publication-title: R package version – volume: 33 start-page: 331 issue: 3 year: 2016 ident: 9427_CR47 publication-title: Journal of Classification doi: 10.1007/s00357-016-9211-9 – volume: 56 start-page: 463 issue: 3 year: 1969 ident: 9427_CR20 publication-title: Biometrika doi: 10.1093/biomet/56.3.463 – ident: 9427_CR62 – ident: 9427_CR67 doi: 10.1109/ICIP.2008.4711718 – volume: 71 start-page: 196 year: 2014 ident: 9427_CR68 publication-title: Computational Statistics and Data Analysis doi: 10.1016/j.csda.2013.07.008 – volume: 32 start-page: 359 issue: 3 year: 2015 ident: 9427_CR69 publication-title: Journal of Classification doi: 10.1007/s00357-015-9188-9 – ident: 9427_CR51 doi: 10.1007/978-3-319-41573-4_18 – volume-title: Modern applied statistics with S, fourth edn. year: 2002 ident: 9427_CR66 doi: 10.1007/978-0-387-21706-2 – ident: 9427_CR43 doi: 10.1214/cbms/1462106013 – volume: 6 start-page: 461 issue: 2 year: 1978 ident: 9427_CR60 publication-title: The Annals of Statistics doi: 10.1214/aos/1176344136 – volume: 27 start-page: 589 issue: 3 year: 1998 ident: 9427_CR30 publication-title: Communications in Statistics-Theory and Methods doi: 10.1080/03610929808832115 – ident: 9427_CR59 – ident: 9427_CR2 doi: 10.1017/S0370164600022070 – volume: 79 start-page: 99 issue: 1 year: 2001 ident: 9427_CR12 publication-title: Journal of Multivariate Analysis doi: 10.1006/jmva.2000.1960 – volume: 22 start-page: 719 issue: 7 year: 2000 ident: 9427_CR8 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/34.865189 – volume: 93 start-page: 18 year: 2016 ident: 9427_CR57 publication-title: Computational Statistics and Data Analysis doi: 10.1016/j.csda.2014.09.006 – volume: 83 start-page: 2088 issue: 9 year: 2013 ident: 9427_CR52 publication-title: Statistics and Probability Letters doi: 10.1016/j.spl.2013.04.011 – volume: 28 start-page: 781 issue: 5 year: 1995 ident: 9427_CR15 publication-title: Pattern Recognition doi: 10.1016/0031-3203(94)00125-6
SSID	ssj0007667
Score	2.3370452
Snippet	Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	145
SubjectTerms	Algorithms Bioinformatics Classification Cluster analysis Clustering Decomposition Eigenvalues Expected values Families & family life Kurtosis Manifolds (mathematics) Marketing Mathematics and Statistics Maximization Mixtures Multivariate analysis Normal distribution Normality Optimization Parameter estimation Pattern Recognition Power Psychometrics Random variables Search algorithms Signal,Image and Speech Processing Skewness Statistical Theory and Methods Statistics
SummonAdditionalLinks	– databaseName: Science Database dbid: M2P link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5B4dAeeCwgFlrkA-ICFokTP3JC0Ic4sNVKgNRbZDuxVLHaXZrt698z4zi7olJ76TFKMnE043l4Hh_Ae5eLIF0VeCFyxdEeK145a7lslLQ2mLLwcbr-D318bE5Oqmk6cOtSWeWgE6Oibhaezsg_FxjJoDOhpPyy_MsJNYqyqwlC4yE8Qs8mp5KuiZiuNbFWqm-X1rgMoUxqmomtc5RC05xq2THAEZrr_w3Txtu8kSCNdufo6X1X_AyeJI-Tfe1F5Dk8aOcj2Ev9CuwDSw1JxCCWdvoIdibrca7dCLanA-DBNV0MOvP6BSwIS23Gv6EtbNj-7JzGLuBvMDunS3TMqRKppx2LE9jk9IpyFh1bBBabfy8wWEd_l_38014ijSmhtrHDq-ViTnVMuPADGu2bULm6l_D76PDX_neeMBy4x8294pUpTItRU5PZsvQ6oC10XuROZE3QZRC6bSpZOtnkmdEu05VryyxYq3JXofPqilewNccvvgZWmsYE6YVzKELKYujuVdtWNsjco1NlxpAPDKx9GnBOOBuzej2aOTK9RqbXkem1HsPH9TvLfrzHnU_vDpyu01bv6g2bx_BpkJXN7dupvbmb2lvYJmj7vkpoF7ZWZ-ftHjz2F6vT7uxdFPR_S5cEEA priority: 102 providerName: ProQuest
Title	Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions
URI	https://link.springer.com/article/10.1007/s00357-022-09427-7 https://www.proquest.com/docview/3254508655
Volume	40
WOSCitedRecordID	wos000933353600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: P5Z dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database (ProQuest) customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: K7- dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: M7S dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: Library Science Database customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: M1O dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/libraryscience providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: BENPR dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: M2O dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1432-1343 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: M2P dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1432-1343 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0007667 issn: 0176-4268 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9QwDLfYxsP2wOAAcbCd8oB4gUht2nz0kW03IcEdFQdo4qVK2kZCnO6mdV_899ht2mkTIMGLpahtkspObMv2zwAvXSy8dJnniYgVR32seOas5bJS0lpv0qRs0fU_6PncnJxkeSgKa_ps9z4k2d7UQ7EbBb00p-xzdEmE5noDtiShzZCPvvg63L9aqa5IWuPiQplQKvP7OW6roxsb805YtNU2x7v_t8-H8CBYl-xtJw6P4F69GsF-qE1gr1goPiJmsHCqR7AzG6BbmxFs531zg5806O9HGpBh2uE6P4Y1NVFb8gNUghU7XF4Q3gLuktkVDdEipxSkbqE2K4HNvl9TsKJha8_aqt9L9NLR0GWLH_UVzpFTuzY2vT5dryiBCf_iiDB9Qzuu5gl8OZ5-PnzHQ_MGXuKpPueZSUyN7lIV2TQttUcl6EoROxFVXqde6LrKZOpkFUdGu0hnrk4jb62KXYZWq0uewuYKV3wGLDWV8bIUzqHsKIs-e6nqOrNexiVaU2YMcc_DogzI5tRgY1kMmMwtTwrkSdHypNBjeD18c9rhevz17b1eNIpwxpsiQd8azVsl5Rje9KJw8_jPsz3_t9dfwDb1uO_ShfZg8_zsot6H--UlsvxsAlsH03n-aQIb7zVHOos_EhUdzYnqBdJcfpu05-QXvUUFkg
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9NAFH4qBYlyYAkgAi3MAbjACHtiz4wPCEEXtWoSIlGk3tyZ8YxUESWhTpf8KX4j73lJBBK99cBxZPvZHn9v89sAXttYhNRmgfdELDnqY8kzawxPC5kaE3TSc1V3_b4aDvXxcTZag19tLQylVbYysRLUxdTRP_IPPfRk0JiQafpp9pPT1CiKrrYjNGpYHPrFJbps5ceDHfy-b4TY2z3a3ufNVAHuEG5znume9mjHF5FJEqcCSmfrRGxFVASVBKF8kaWJTYs40spGKrM-iYIxMrYZmlO2h3RvwW3qq0ccNYi_LiW_krIuz1b42kLqpkinKtWjkJ3ilDuPDpVQXP2pCFfW7V8B2UrP7T3433boIdxvLGr2uWaBR7DmJx3Yauox2FvWFFwRAFkjyTpwb7BsV1t2YGPUDnRY0KLVCYvHMKVZcWP-BXV9wbbH59RWAreNmQkt0fGgTKuadpV8wQanVxSTKdk0sKq4-cIgi889-_bDXyKNEU2lY7tXs-mE8rTwwXeodXEzdax8At9vZK-ewvoE7_gMWKILHVInrEUWkUZr7aT3mQlp7NBo1F2IW8DkrmngTnNExvmy9XQFshxBllcgy1UX3i2vmdXtS649e7NFVt6IsjJfwaoL71tsrg7_m9rz66m9grv7R4N-3j8YHr6ADYHGY50RtQnr87NzvwV33MX8tDx7WTEZg5Obxuxv0pBgLA
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BQag98FhAXWjBB8QFrCZO_MgR2q5AtKsVBdRbZDuxhFhlV91tKf-emcRJAQES4mglsR3N2PONZuYbgGcuFUG6IvBMpIqjPVa8cNZyWSlpbTB55lt2_SM9nZrT02L2QxV_m-3ehyS7mgZiaWrWe8sq7A2FbxQA05wy0dE9EZrr63AjR0-Gkrren3wa7mKtVFcwrXEjQplYNvP7OX42TVd485cQaWt5Jnf-f8934XZEnexVpyb34FrdjGA31iyw5ywWJZGQWDztI9g6HihdVyPYnPVND77RoL83aUCAteN7vg8Laq4256_ROFZsf35OPAy4Y2YbGiJSp9SkbqE2W4Edf76kIMaKLQJrq4Ev0HtHAMxOvtRfcY4ZtXFjh5fLRUOJTfgXB8T1G9t0rR7Ax8nhh_03PDZ14B5P-5oXJjM1ulFVYvPc64DG0XmROpFUQedB6LoqZO5klSZGu0QXrs6TYK1KXYFo1mUPYaPBFbeB5aYyQXrhHOqUsujLe1XXhQ0y9YiyzBjSXp6lj4zn1HhjXg5cza1MSpRJ2cqk1GN4MXyz7Pg-_vr2Tq8mZTz7qzJDnxthr5JyDC97tbh6_OfZHv3b60_h1uxgUh69nb57DJsCwVeXUbQDG-uz83oXbvoLlP7Zk_ZIfAcqeAor
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Model-Based+Clustering+and+Classification+Using+Mixtures+of+Multivariate+Skewed+Power+Exponential+Distributions&rft.jtitle=Journal+of+classification&rft.au=Dang%2C+Utkarsh+J.&rft.au=Gallaugher%2C+Michael+P.B.&rft.au=Browne%2C+Ryan+P.&rft.au=McNicholas%2C+Paul+D.&rft.date=2023-04-01&rft.pub=Springer+US&rft.issn=0176-4268&rft.eissn=1432-1343&rft.volume=40&rft.issue=1&rft.spage=145&rft.epage=167&rft_id=info:doi/10.1007%2Fs00357-022-09427-7&rft.externalDocID=10_1007_s00357_022_09427_7
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0176-4268&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0176-4268&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0176-4268&client=summon