{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:39:27Z","timestamp":1772167167342,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T00:00:00Z","timestamp":1603756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005743","name":"Universit\u00e0 Cattolica del Sacro Cuore","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005743","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Stat"],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many statistical problems involve the estimation of a<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\left( d\\times d\\right) $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mfenced><mml:mi>d<\/mml:mi><mml:mo>\u00d7<\/mml:mo><mml:mi>d<\/mml:mi><\/mml:mfenced><\/mml:math><\/jats:alternatives><\/jats:inline-formula>orthogonal matrix<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{Q}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>Q<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Such an estimation is often challenging due to the orthonormality constraints on<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{Q}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>Q<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. To cope with this problem, we use the well-known PLU decomposition, which factorizes any invertible<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\left( d\\times d\\right) $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mfenced><mml:mi>d<\/mml:mi><mml:mo>\u00d7<\/mml:mo><mml:mi>d<\/mml:mi><\/mml:mfenced><\/mml:math><\/jats:alternatives><\/jats:inline-formula>matrix as the product of a<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\left( d\\times d\\right) $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mfenced><mml:mi>d<\/mml:mi><mml:mo>\u00d7<\/mml:mo><mml:mi>d<\/mml:mi><\/mml:mfenced><\/mml:math><\/jats:alternatives><\/jats:inline-formula>permutation matrix<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{P}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>P<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, a<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\left( d\\times d\\right) $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mfenced><mml:mi>d<\/mml:mi><mml:mo>\u00d7<\/mml:mo><mml:mi>d<\/mml:mi><\/mml:mfenced><\/mml:math><\/jats:alternatives><\/jats:inline-formula>unit lower triangular matrix<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{L}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, and a<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\left( d\\times d\\right) $$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mfenced><mml:mi>d<\/mml:mi><mml:mo>\u00d7<\/mml:mo><mml:mi>d<\/mml:mi><\/mml:mfenced><\/mml:math><\/jats:alternatives><\/jats:inline-formula>upper triangular matrix<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{U}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>U<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Thanks to the QR decomposition, we find the formulation of<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{U}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>U<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>when the PLU decomposition is applied to<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{Q}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>Q<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. We call the result as PLR decomposition; it produces a one-to-one correspondence between<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{Q}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>Q<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>and the<jats:inline-formula><jats:alternatives><jats:tex-math>$$d\\left( d-1\\right) \/2$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>d<\/mml:mi><mml:mfenced><mml:mi>d<\/mml:mi><mml:mo>-<\/mml:mo><mml:mn>1<\/mml:mn><\/mml:mfenced><mml:mo>\/<\/mml:mo><mml:mn>2<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>entries below the diagonal of<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{L}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>, which are advantageously unconstrained real values. Thus, once the decomposition is applied, regardless of the objective function under consideration, we can use any classical unconstrained optimization method to find the minimum (or maximum) of the objective function with respect to<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{L}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. For illustrative purposes, we apply the PLR decomposition in common principle components analysis (CPCA) for the maximum likelihood estimation of the common orthogonal matrix when a multivariate leptokurtic-normal distribution is assumed in each group. Compared to the commonly used normal distribution, the leptokurtic-normal has an additional parameter governing the excess kurtosis; this makes the estimation of<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\varvec{Q}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mi>Q<\/mml:mi><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>in CPCA more robust against mild outliers. The usefulness of the PLR decomposition in leptokurtic-normal CPCA is illustrated by two biometric data analyses.<\/jats:p>","DOI":"10.1007\/s00180-020-01041-8","type":"journal-article","created":{"date-parts":[[2020,10,27]],"date-time":"2020-10-27T13:03:38Z","timestamp":1603803818000},"page":"1177-1195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Unconstrained representation of orthogonal matrices with application to common principal components"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3129-5385","authenticated-orcid":false,"given":"Luca","family":"Bagnato","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7742-1821","authenticated-orcid":false,"given":"Antonio","family":"Punzo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,27]]},"reference":[{"issue":"3","key":"1041_CR1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1006\/jtbi.1995.0242","volume":"177","author":"J-P Airoldi","year":"1995","unstructured":"Airoldi J-P, Flury B, Salvioni M (1995) Discrimination between two species of microtususing both classified and unclassified observations. J Theor Biol 177(3):247\u2013262","journal-title":"J Theor Biol"},{"issue":"6","key":"1041_CR2","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H Akaike","year":"1974","unstructured":"Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716\u2013723","journal-title":"IEEE Trans Autom Control"},{"issue":"5","key":"1041_CR3","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s11222-011-9272-x","volume":"22","author":"JL Andrews","year":"2012","unstructured":"Andrews JL, McNicholas PD (2012) Model-based clustering, classification, and discriminant analysis with the multivariate $$t$$-distribution: the $$t$$EIGEN family. Stat Comput 22(5):1021\u20131029","journal-title":"Stat Comput"},{"issue":"6","key":"1041_CR4","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1080\/03610918.2012.735318","volume":"43","author":"L Bagnato","year":"2014","unstructured":"Bagnato L, Greselin F, Punzo A (2014) On the spectral decomposition in normal discriminant analysis. Commun Stat Simul Comput 43(6):1471\u20131489","journal-title":"Commun Stat Simul Comput"},{"issue":"1","key":"1041_CR5","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/cjs.11308","volume":"45","author":"L Bagnato","year":"2017","unstructured":"Bagnato L, Punzo A, Zoia MG (2017) The multivariate leptokurtic-normal distribution and its application in model-based clustering. Can J Stat 45(1):95\u2013119","journal-title":"Can J Stat"},{"key":"1041_CR6","doi-asserted-by":"crossref","DOI":"10.1201\/b17040","volume-title":"Linear algebra and matrix analysis for statistics","author":"S Banerjee","year":"2014","unstructured":"Banerjee S, Roy A (2014) Linear algebra and matrix analysis for statistics. CRC Press, Boca Raton"},{"issue":"3","key":"1041_CR7","doi-asserted-by":"crossref","first-page":"803","DOI":"10.2307\/2532201","volume":"49","author":"JD Banfield","year":"1993","unstructured":"Banfield JD, Raftery AE (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics 49(3):803\u2013821","journal-title":"Biometrics"},{"key":"1041_CR8","doi-asserted-by":"crossref","unstructured":"Boente G, Orellana L (2001) A robust approach to common principal components. In: Statistics in genetics and in the environmental sciences, trends in mathematics, Springer, Birkh\u00e4user, pp 117\u2013145","DOI":"10.1007\/978-3-0348-8326-9_9"},{"issue":"4","key":"1041_CR9","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1093\/biomet\/89.4.861","volume":"89","author":"G Boente","year":"2002","unstructured":"Boente G, Pires AM, Rodrigues IM (2002) Influence functions and outlier detection under the common principal components model: a robust approach. Biometrika 89(4):861\u2013875","journal-title":"Biometrika"},{"issue":"1","key":"1041_CR10","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.jmva.2004.11.007","volume":"97","author":"G Boente","year":"2006","unstructured":"Boente G, Pires AM, Rodrigues IM (2006) General projection-pursuit estimators for the common principal components model: influence functions and Monte Carlo study. J Multivar Anal 97(1):124\u2013147","journal-title":"J Multivar Anal"},{"issue":"4","key":"1041_CR11","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1016\/j.jspi.2008.05.052","volume":"139","author":"G Boente","year":"2009","unstructured":"Boente G, Pires AM, Rodrigues IM (2009) Robust tests for the common principal components model. J Stat Plan Inference 139(4):1332\u20131347","journal-title":"J Stat Plan Inference"},{"issue":"1","key":"1041_CR12","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1093\/biomet\/89.1.159","volume":"89","author":"RJ Boik","year":"2002","unstructured":"Boik RJ (2002) Spectral models for covariance matrices. Biometrika 89(1):159\u2013182","journal-title":"Biometrika"},{"key":"1041_CR13","volume-title":"Morphometric tools for landmark data: geometry and biology. geometry and biology","author":"FL Bookstein","year":"1997","unstructured":"Bookstein FL (1997) Morphometric tools for landmark data: geometry and biology. geometry and biology. Cambridge University Press, Cambridge"},{"issue":"5","key":"1041_CR14","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 Recogn 28(5):781\u2013793","journal-title":"Pattern Recogn"},{"issue":"4","key":"1041_CR15","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1111\/biom.12351","volume":"71","author":"UJ Dang","year":"2015","unstructured":"Dang UJ, Browne RP, McNicholas PD (2015) Mixtures of multivariate power exponential distributions. Biometrics 71(4):1081\u20131089","journal-title":"Biometrics"},{"issue":"3","key":"1041_CR16","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1080\/00949655.2018.1554659","volume":"89","author":"F Dotto","year":"2019","unstructured":"Dotto F, Farcomeni A (2019) Robust inference for parsimonious model-based clustering. J Stat Comput Simul 89(3):414\u2013442","journal-title":"J Stat Comput Simul"},{"key":"1041_CR17","volume-title":"Multivariate statistics: a practical approach","author":"B Flury","year":"2011","unstructured":"Flury B (2011) Multivariate statistics: a practical approach. Springer, Dordrecht"},{"key":"1041_CR18","unstructured":"Flury B (2012) Flury: data sets from flury, 1997. R package version 0.1-3"},{"key":"1041_CR19","series-title":"Springer texts in statistics","volume-title":"A first course in multivariate statistics","author":"B Flury","year":"2013","unstructured":"Flury B (2013) A first course in multivariate statistics. Springer texts in statistics. Springer, New York"},{"issue":"1","key":"1041_CR20","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/0167-7152(86)90035-0","volume":"4","author":"BK Flury","year":"1986","unstructured":"Flury BK (1986a) Proportionality of $$k$$ covariance matrices. Stat Probab Lett 4(1):29\u201333","journal-title":"Stat Probab Lett"},{"issue":"1","key":"1041_CR21","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1093\/biomet\/74.1.59","volume":"74","author":"BK Flury","year":"1987","unstructured":"Flury BK (1987) Two generalizations of the common principal component model. Biometrika 74(1):59\u201369","journal-title":"Biometrika"},{"issue":"388","key":"1041_CR22","first-page":"892","volume":"79","author":"BN Flury","year":"1984","unstructured":"Flury BN (1984) Common principal components in $$k$$ groups. J Am Stat Assoc 79(388):892\u2013898","journal-title":"J Am Stat Assoc"},{"key":"1041_CR23","doi-asserted-by":"crossref","unstructured":"Flury BN (1986b) Asymptotic theory for common principal component analysis. In: The annals of statistics, pp 418\u2013430","DOI":"10.1214\/aos\/1176349930"},{"key":"1041_CR24","volume-title":"Common principal components and related multivariate models","author":"BN Flury","year":"1988","unstructured":"Flury BN (1988) Common principal components and related multivariate models. Wiley, New York"},{"key":"1041_CR25","doi-asserted-by":"crossref","first-page":"177","DOI":"10.2307\/2347375","volume":"35","author":"BN Flury","year":"1985","unstructured":"Flury BN, Constantine G (1985) The F-G diagonalization algorithm. Appl Stat 35:177\u2013183","journal-title":"Appl Stat"},{"issue":"1","key":"1041_CR26","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1137\/0907013","volume":"7","author":"BN Flury","year":"1986","unstructured":"Flury BN, Gautschi W (1986) An algorithm for simultaneous orthogonal transformation of several positive definite matrices to nearly diagonal form. SIAM J Sci Stat Comput 7(1):169\u2013184","journal-title":"SIAM J Sci Stat Comput"},{"issue":"1","key":"1041_CR27","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/BF01201025","volume":"11","author":"BW Flury","year":"1994","unstructured":"Flury BW, Schmid MJ, Narayanan A (1994) Error rates in quadratic discrimination with constraints on the covariance matrices. J Classif 11(1):101\u2013120","journal-title":"J Classif"},{"issue":"6","key":"1041_CR28","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1007\/s11222-013-9414-4","volume":"24","author":"F Forbes","year":"2014","unstructured":"Forbes F, Wraith D (2014) A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: application to robust clustering. Stat Comput 24(6):971\u2013984","journal-title":"Stat Comput"},{"issue":"458","key":"1041_CR29","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1198\/016214502760047131","volume":"97","author":"C Fraley","year":"2002","unstructured":"Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97(458):611\u2013631","journal-title":"J Am Stat Assoc"},{"key":"1041_CR30","unstructured":"Graybill FA (1976) An introduction to linear statistical models, McGraw-Hill series in probability and statistics, vol 1, McGraw-Hill"},{"issue":"2","key":"1041_CR31","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s10260-010-0157-5","volume":"20","author":"F Greselin","year":"2011","unstructured":"Greselin F, Ingrassia S, Punzo A (2011) Assessing the pattern of covariance matrices via an augmentation multiple testing procedure. Stat Methods Appl 20(2):141\u2013170","journal-title":"Stat Methods Appl"},{"issue":"3","key":"1041_CR32","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/00031305.2013.791643","volume":"67","author":"F Greselin","year":"2013","unstructured":"Greselin F, Punzo A (2013) Closed likelihood ratio testing procedures to assess similarity of covariance matrices. Am Stat 67(3):117\u2013128","journal-title":"Am Stat"},{"issue":"7","key":"1041_CR33","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1080\/10485250903548737","volume":"22","author":"M Hallin","year":"2010","unstructured":"Hallin M, Paindaveine D, Verdebout T (2010) Testing for common principal components under heterokurticity. J Nonparametric Stat 22(7):879\u2013895","journal-title":"J Nonparametric Stat"},{"key":"1041_CR34","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198507031.001.0001","volume-title":"Matrices for statistics","author":"MJR Healy","year":"2000","unstructured":"Healy MJR (2000) Matrices for statistics. Oxford Science Publications, Clarendon Press"},{"key":"1041_CR35","doi-asserted-by":"crossref","DOI":"10.56021\/9780801846595","volume-title":"Problems of relative growth","author":"J Huxley","year":"1993","unstructured":"Huxley J (1993) Problems of relative growth. Methuen, London"},{"issue":"1","key":"1041_CR36","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/aoms\/1177728846","volume":"25","author":"AT James","year":"1954","unstructured":"James AT (1954) Normal multivariate analysis and the orthogonal group. Ann Math Stat 25(1):40\u201375","journal-title":"Ann Math Stat"},{"issue":"3","key":"1041_CR37","doi-asserted-by":"crossref","first-page":"497","DOI":"10.2307\/2527939","volume":"19","author":"P Jolicoeur","year":"1963","unstructured":"Jolicoeur P (1963) The multivariate generalization of the allometry equation. Biometrics 19(3):497\u2013499","journal-title":"Biometrics"},{"key":"1041_CR38","doi-asserted-by":"crossref","unstructured":"Khuri AI (2003) Advanced calculus with applications in statistics. Wiley series in probability and statistics, Wiley","DOI":"10.1002\/0471394882"},{"issue":"2","key":"1041_CR39","first-page":"231","volume":"23","author":"AI Khuri","year":"1989","unstructured":"Khuri AI, Good IJ (1989) The parameterization of orthogonal matrices: a review mainly for statisticians. S Afr Stat J 23(2):231\u2013250","journal-title":"S Afr Stat J"},{"key":"1041_CR40","doi-asserted-by":"crossref","unstructured":"Klingenberg CP (1996) Multivariate allometry. In: Advances in morphometrics, NATO ASI series (series A: life sciences), vol 284, Boston, Springer, pp 23\u201349","DOI":"10.1007\/978-1-4757-9083-2_3"},{"issue":"2","key":"1041_CR41","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1093\/sysbio\/45.2.135","volume":"45","author":"CP Klingenberg","year":"1996","unstructured":"Klingenberg CP, Neuenschwander BE, Flury BD (1996) Ontogeny and individual variation: analysis of patterned covariance matrices with common principal components. Syst Biol 45(2):135\u2013150","journal-title":"Syst Biol"},{"key":"1041_CR42","unstructured":"Korkmaz S, Goksuluk D, Zararsiz G (2019) MVN: multivariate normality tests. R package version 5.6"},{"issue":"2","key":"1041_CR43","first-page":"164","volume":"33","author":"WJ Krzanowski","year":"1984","unstructured":"Krzanowski WJ (1984) Principal component analysis in the presence of group structure. J R Stat Soc Ser C (Appl Stat) 33(2):164\u2013168","journal-title":"J R Stat Soc Ser C (Appl Stat)"},{"key":"1041_CR44","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.csda.2013.02.020","volume":"71","author":"T-I Lin","year":"2014","unstructured":"Lin T-I (2014) Learning from incomplete data via parameterized t mixture models through eigenvalue decomposition. Comput Stat Data Anal 71:183\u2013195","journal-title":"Comput Stat Data Anal"},{"key":"1041_CR45","volume-title":"Handbook of matrices","author":"H L\u00fctkepohl","year":"1996","unstructured":"L\u00fctkepohl H (1996) Handbook of matrices. Wiley, Chicester"},{"issue":"1","key":"1041_CR46","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1093\/jjfinec\/nby019","volume":"17","author":"A Maruotti","year":"2019","unstructured":"Maruotti A, Punzo A, Bagnato L (2019) Hidden Markov and semi-Markov models with multivariate leptokurtic-normal components for robust modeling of daily returns series. J Financ Econom 17(1):91\u2013117","journal-title":"J Financ Econom"},{"issue":"2","key":"1041_CR47","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1007\/s00362-017-0964-y","volume":"61","author":"A Mazza","year":"2020","unstructured":"Mazza A, Punzo A (2020) Mixtures of multivariate contaminated normal regression models. Stat Pap 61(2):787\u2013822","journal-title":"Stat Pap"},{"key":"1041_CR48","volume-title":"Morphometrics, the multivariate analysis of biological data","author":"RA Pimentel","year":"1979","unstructured":"Pimentel RA (1979) Morphometrics, the multivariate analysis of biological data. Kendall\/Hunt Pub. Co., Dubuque"},{"issue":"3","key":"1041_CR49","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1093\/biomet\/86.3.677","volume":"86","author":"M Pourahmadi","year":"1999","unstructured":"Pourahmadi M (1999) Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation. Biometrika 86(3):677\u2013690","journal-title":"Biometrika"},{"issue":"2","key":"1041_CR50","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1093\/biomet\/87.2.425","volume":"87","author":"M Pourahmadi","year":"2000","unstructured":"Pourahmadi M (2000) Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix. Biometrika 87(2):425\u2013435","journal-title":"Biometrika"},{"issue":"3","key":"1041_CR51","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.jmva.2005.11.002","volume":"98","author":"M Pourahmadi","year":"2007","unstructured":"Pourahmadi M, Daniels M, Park T (2007) Simultaneous modelling of the Cholesky decomposition of several covariance matrices. J Multivar Anal 98(3):568\u2013587","journal-title":"J Multivar Anal"},{"key":"1041_CR52","doi-asserted-by":"publisher","DOI":"10.1080\/00949655.2020.1805451","author":"A Punzo","year":"2020","unstructured":"Punzo A, Bagnato L (2020) The multivariate tail-inflated normal distribution and its application in finance. J Stat Comput Simul. https:\/\/doi.org\/10.1080\/00949655.2020.1805451","journal-title":"J Stat Comput Simul"},{"issue":"14","key":"1041_CR53","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1080\/00949655.2015.1131282","volume":"86","author":"A Punzo","year":"2016","unstructured":"Punzo A, Browne RP, McNicholas PD (2016) Hypothesis testing for mixture model selection. J Stat Comput Simul 86(14):2797\u20132818","journal-title":"J Stat Comput Simul"},{"key":"1041_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v085.i10","volume":"85","author":"A Punzo","year":"2018","unstructured":"Punzo A, Mazza A, McNicholas PD (2018) ContaminatedMixt: an R package for fitting parsimonious mixtures of multivariate contaminated normal distributions. J Stat Softw 85:1\u201325","journal-title":"J Stat Softw"},{"issue":"6","key":"1041_CR55","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1002\/bimj.201500144","volume":"58","author":"A Punzo","year":"2016","unstructured":"Punzo A, McNicholas PD (2016) Parsimonious mixtures of multivariate contaminated normal distributions. Biom J 58(6):1506\u20131537","journal-title":"Biom J"},{"key":"1041_CR56","doi-asserted-by":"publisher","DOI":"10.1177\/1471082X19890935","author":"A Punzo","year":"2019","unstructured":"Punzo A, Tortora C (2019) Multiple scaled contaminated normal distribution and its application in clustering. Stat Model. https:\/\/doi.org\/10.1177\/1471082X19890935","journal-title":"Stat Model"},{"key":"1041_CR57","unstructured":"R Core Team (2018) R: a language and environment for statistical computing, textsfR Foundation for Statistical Computing, Vienna"},{"key":"1041_CR58","volume-title":"Multidimensional palaeobiology","author":"RA Reyment","year":"1991","unstructured":"Reyment RA (1991) Multidimensional palaeobiology. Pergamon Press, Oxford"},{"key":"1041_CR59","unstructured":"Ritter G (2015) Robust cluster analysis and variable selection, Chapman & Hall\/CRC monographs on statistics and applied probability, vol 137, CRC Press"},{"key":"1041_CR60","unstructured":"Schott JR (2016) Matrix analysis for statistics. Wiley series in probability and statistics, Wiley"},{"issue":"2","key":"1041_CR61","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. Ann Stat 6(2):461\u2013464","journal-title":"Ann Stat"},{"key":"1041_CR62","unstructured":"Searle SR, Khuri AI (2017) Matrix algebra useful for statistics. Wiley series in probability and statistics, Wiley"},{"issue":"12","key":"1041_CR63","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1016\/j.csda.2010.03.010","volume":"54","author":"NT Trendafilov","year":"2010","unstructured":"Trendafilov NT (2010) Stepwise estimation of common principal components. Comput Stat Data Anal 54(12):3446\u20133457","journal-title":"Comput Stat Data Anal"},{"key":"1041_CR64","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 PD (2014) Parsimonious skew mixture models for model-based clustering and classification. Comput Stat Data Anal 71:196\u2013210","journal-title":"Comput Stat Data Anal"}],"container-title":["Computational Statistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-020-01041-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00180-020-01041-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-020-01041-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T13:48:22Z","timestamp":1723816102000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00180-020-01041-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,27]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1041"],"URL":"https:\/\/doi.org\/10.1007\/s00180-020-01041-8","relation":{},"ISSN":["0943-4062","1613-9658"],"issn-type":[{"value":"0943-4062","type":"print"},{"value":"1613-9658","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,10,27]]},"assertion":[{"value":"8 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}