cfda: an R Package for Categorical Functional ...
Type de document :
Pré-publication ou Document de travail
Titre :
cfda: an R Package for Categorical Functional Data Analysis
Auteur(s) :
Preda, Cristian [Auteur]
Romanian Academy
MOdel for Data Analysis and Learning [MODAL]
Grimonprez, Quentin [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Vandewalle, Vincent [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]

Romanian Academy
MOdel for Data Analysis and Learning [MODAL]
Grimonprez, Quentin [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Vandewalle, Vincent [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Mot(s)-clé(s) en anglais :
functional data
categorical data
stochastic process
multiple correspondence analysis
categorical data
stochastic process
multiple correspondence analysis
Discipline(s) HAL :
Statistiques [stat]
Statistiques [stat]/Méthodologie [stat.ME]
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [en]
Categorical functional data represented by paths of a stochastic jump process with continuous time and finite set of states are considered. As an extension of the multiple correspondence analysis to an infinite set of ...
Lire la suite >Categorical functional data represented by paths of a stochastic jump process with continuous time and finite set of states are considered. As an extension of the multiple correspondence analysis to an infinite set of variables, optimal encodings of states over time are approximated using an arbitrary finite basis of functions. That allows dimension reduction, optimal representation and visualisation of data in lower dimensional spaces. The methodology is implemented in the cfda R package and is illustrated using a real data set in the clustering framework.Lire moins >
Lire la suite >Categorical functional data represented by paths of a stochastic jump process with continuous time and finite set of states are considered. As an extension of the multiple correspondence analysis to an infinite set of variables, optimal encodings of states over time are approximated using an arbitrary finite basis of functions. That allows dimension reduction, optimal representation and visualisation of data in lower dimensional spaces. The methodology is implemented in the cfda R package and is illustrated using a real data set in the clustering framework.Lire moins >
Langue :
Anglais
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