Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables
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Fabrizio Durante | Sebastian Fuchs | F. Marta L. Di Lascio | F. Durante | S. Fuchs | F. M. L. D. Lascio
[1] Claudia Czado,et al. Selecting and estimating regular vine copulae and application to financial returns , 2012, Comput. Stat. Data Anal..
[2] Paola Zuccolotto,et al. A double clustering algorithm for financial time series based on extreme events , 2016 .
[3] Andrea Bonanomi,et al. Dissimilarity measure for ranking data via mixture of copulae , 2019, Stat. Anal. Data Min..
[4] R. Nelsen. Concordance and Copulas: A Survey , 2002 .
[5] A. Müller,et al. Some Remarks on the Supermodular Order , 2000 .
[6] Sebastian Fuchs,et al. Characterizations of Copulas Attaining the Bounds of Multivariate Kendall’s Tau , 2018, J. Optim. Theory Appl..
[7] A. D. Gordon. A Review of Hierarchical Classification , 1987 .
[8] Johan Segers,et al. Measuring association and dependence between random vectors , 2014, J. Multivar. Anal..
[9] Manuel Úbeda-Flores. Multivariate versions of Blomqvist’s beta and Spearman’s footrule , 2005 .
[10] Friedrich Schmid,et al. Multivariate conditional versions of Spearman's rho and related measures of tail dependence , 2007 .
[11] A. Hall. Methods for showing Distinctness and aiding Identification of Critical Groups in Taxonomy and Ecology , 1968, Nature.
[12] M. D. Taylor. Multivariate measures of concordance for copulas and their marginals , 2010, 1004.5023.
[13] Sartaj Sahni,et al. Linear space string correction algorithm using the Damerau-Levenshtein distance , 2020, BMC Bioinform..
[14] F. Marta L. Di Lascio,et al. Clustering dependent observations with copula functions , 2015 .
[15] Ruodu Wang,et al. Extremal Dependence Concepts , 2015, 1512.03232.
[16] Paul Embrechts,et al. A note on generalized inverses , 2013, Math. Methods Oper. Res..
[17] Christian Genest,et al. A copula‐based risk aggregation model , 2015 .
[18] Sebastian Fuchs,et al. On Minimal Copulas under the Concordance Order , 2018, J. Optim. Theory Appl..
[19] Andrea Cavalli,et al. A Comparative Study on the Application of Hierarchical-Agglomerative Clustering Approaches to Organize Outputs of Reiterated Docking Runs , 2006, J. Chem. Inf. Model..
[20] Inge Koch,et al. Measuring Comonotonicity in M-Dimensional Vectors , 2011, ASTIN Bulletin.
[21] Christian Genest,et al. Copula parameter estimation using Blomqvist’s beta , 2013 .
[22] A Biconvex Form for Copulas , 2016 .
[23] Paola Zuccolotto,et al. Dynamic tail dependence clustering of financial time series , 2017 .
[24] B. Liseo,et al. Portfolio Diversification Strategy Via Tail-Dependence Clustering and ARMA-GARCH Vine Copula Approach , 2018, Australian Economic Papers.
[25] Giovanni De Luca,et al. A tail dependence-based dissimilarity measure for financial time series clustering , 2011, Adv. Data Anal. Classif..
[26] Guy Perrière,et al. MADE4: an R package for multivariate analysis of gene expression data , 2005, Bioinform..
[27] S. Fuchs. Transformations of Copulas and Measures of Concordance , 2015 .
[28] Marco Scarsini,et al. On measures of concordance , 1984 .
[29] D. Botstein,et al. Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[30] Fabrizio Durante,et al. Clustering of time series via non-parametric tail dependence estimation , 2015 .
[31] Fabrizio Durante,et al. Copula–based clustering methods , 2017 .
[32] Irène Gijbels,et al. On the specification of multivariate association measures and their behaviour with increasing dimension , 2021, J. Multivar. Anal..
[33] Thierry Duchesne,et al. Detection of block-exchangeable structure in large-scale correlation matrices , 2017, J. Multivar. Anal..
[34] Fionn Murtagh,et al. Handbook of Cluster Analysis , 2015 .
[35] Friedrich Schmid,et al. Copula-Based Measures of Multivariate Association , 2010 .
[36] S. Fuchs. Copula–Induced Measures of Concordance , 2016 .
[37] Ivan Kojadinovic,et al. Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages , 2010, Comput. Stat. Data Anal..
[38] Dimitris Karlis,et al. Model-based clustering using copulas with applications , 2014, Statistics and Computing.
[39] Marco Scarsini,et al. Multivariate comonotonicity , 2010, J. Multivar. Anal..
[40] U. Alon,et al. Transcriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. , 2001, Cancer research.
[41] Nivedita Deo,et al. Correlation and network analysis of global financial indices. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[42] E. Regazzini,et al. On the centennial anniversary of Gini’s theory of statistical relations , 2017 .
[43] G. Caldarelli,et al. Networks of equities in financial markets , 2004 .
[44] Ivan Kojadinovic,et al. Agglomerative hierarchical clustering of continuous variables based on mutual information , 2004, Comput. Stat. Data Anal..
[45] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[46] Guy Perrière,et al. Cross-platform comparison and visualisation of gene expression data using co-inertia analysis , 2003, BMC Bioinformatics.
[47] M. Scherer,et al. Vandu el Distributions with given marginals : the beginnings An interview with , 2016 .
[48] Pierpaolo D'Urso,et al. Copula-based fuzzy clustering of spatial time series , 2017 .
[49] Chen Yang,et al. Clustering of financial instruments using jump tail dependence coefficient , 2018, Stat. Methods Appl..
[50] Jan Dhaene,et al. The Concept of Comonotonicity in Actuarial Science and Finance: Theory , 2002, Insurance: Mathematics and Economics.
[51] Claudia Czado,et al. Maximum likelihood estimation of mixed C-vines with application to exchange rates , 2012 .
[52] H. Joe,et al. Flexible copula models with dynamic dependence and application to financial data , 2020 .
[53] L. Hubert,et al. Comparing partitions , 1985 .
[54] Elif F. Acar,et al. Flexible dynamic vine copula models for multivariate time series data , 2019, Econometrics and Statistics.
[55] C. Sempi,et al. Principles of Copula Theory , 2015 .
[56] Camille Roth,et al. Natural Scales in Geographical Patterns , 2017, Scientific Reports.
[57] Christian A. Rees,et al. Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.
[58] C. Biernacki,et al. Model-based clustering of Gaussian copulas for mixed data , 2014, 1405.1299.
[59] M. Hofert,et al. Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas , 2017 .
[60] William M. Rand,et al. Objective Criteria for the Evaluation of Clustering Methods , 1971 .
[61] C. Genest,et al. ESTIMATORS BASED ON KENDALL'S TAU IN MULTIVARIATE COPULA MODELS , 2011 .
[62] Andrew J. Patton. A review of copula models for economic time series , 2012, J. Multivar. Anal..
[63] Fabrizio Durante,et al. Clustering of financial time series in risky scenarios , 2013, Advances in Data Analysis and Classification.
[64] Harry Joe,et al. Multivariate concordance , 1990 .
[65] J. V. Ness,et al. Admissible clustering procedures , 1971 .
[66] Brian Everitt,et al. Cluster analysis , 1974 .