Mtcopula: Synthetic Complex Data Generation Using Copula Fodil Benali, Damien Bodénès, Nicolas Labroche, Cyril De Runz
MTCopula: Synthetic Complex Data Generation Using Copula Fodil Benali, Damien Bodénès, Nicolas Labroche, Cyril de Runz To cite this version: Fodil Benali, Damien Bodénès, Nicolas Labroche, Cyril de Runz. MTCopula: Synthetic Complex Data Generation Using Copula. 23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP), 2021, Nicosia, Cyprus. pp.51-60. hal-03188317 HAL Id: hal-03188317 https://hal.archives-ouvertes.fr/hal-03188317 Submitted on 1 Apr 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. MTCopula: Synthetic Complex Data Generation Using Copula Fodil Benali, Damien Bodénès Nicolas Labroche, Cyril de Runz Adwanted Group BDTLN - LIFAT, University of Tours Paris, France Blois, France {fbenali,dbodenes}@adwanted.com {nicolas.labroche,cyril.derunz}@univ-tours.fr ABSTRACT Nevertheless, recently, there has been a growing interest in Nowadays, marketing strategies are data-driven, and their quality Copula-based models for estimating [1, 26] and sampling [10, 29] depends significantly on the quality and quantity of available data. from a multivariate distribution function. Copula [15] are joint As it is not always possible to access this data, there is a need for probability distributions in which any univariate continuous synthetic data generation.
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