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NOEL Cédric

University of Luxembourg > Faculty of Science, Technology and Medecine (FSTM)

Main Referenced Co-authors
SCHILTZ, Jean  (16)
GUIGOU, Jean-Daniel  (1)
Main Referenced Keywords
Finite Mixture Models (1); Identifiability (1); Normal Distribution (1);
Main Referenced Disciplines
Mathematics (7)
Quantitative methods in economics & management (6)
Finance (2)
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others (1)

Publications (total 16)

The most downloaded
574 downloads
NOEL, C., & SCHILTZ, J. (2022). trajeR, an R package for cluster analysis of time series. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/50962. https://hdl.handle.net/10993/50962

SCHILTZ, J., & NOEL, C. (18 July 2023). Finite Mixture Models For An Underlying Beta Distribution With An Application To COVID-19 Data [Paper presentation]. 64th world statistics congress.
Peer reviewed

SCHILTZ, J., & NOEL, C. (04 July 2023). Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 data [Paper presentation]. 34th European Meeting of Statisticians, Warsaw, Poland.

NOEL, C., & SCHILTZ, J. (08 June 2023). Multiple Trajectory Analysis in Finite Mixture Modeling [Paper presentation]. ASMDA 2023, Heraklion, Greece.

NOEL, C., & SCHILTZ, J. (02 June 2023). A new R package for Finite Mixture Models with an application to clustering countries with respect to COVID data [Paper presentation]. 2023 Africa Meeting of the Econometric Society, Nairobi, Kenya.

SCHILTZ, J., & NOEL, C. (05 January 2023). New results in finite mixture modeling [Paper presentation]. Luxembourg-Waseda workshop on Models and Inference for Complex Data, Belval, Luxembourg.

NOEL, C., & SCHILTZ, J. (14 September 2022). Modèles de mélanges finis pour une distribution de loi BETA sous-jacente avec une application à des données sur la COVID-19 [Paper presentation]. 27èmes Rencontres de la Société Francophone de Classification, Lyon, France.

NOEL, C., & SCHILTZ, J. (10 June 2022). A new R package for Finite Mixture Models with an application to clustering countries with respect to COVID data [Paper presentation]. 7th Stochastic Modeling Techniques and Data Analysis International Conference, Athens, Greece.

SCHILTZ, J., NOEL, C., & GUIGOU, J.-D. (20 April 2022). A new R package for Finite Mixture Models with an application to pension systems [Paper presentation]. 10th Conference on Mathematical and Statistical Methods for Actuarial Sciences and Finance, Salerno, Salerno, Italy.

SCHILTZ, J., & NOEL, C. (2022). Finite Mixture Models for an underlying Beta distribution with an application to COVID-19 data. (1). ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/50948.

NOEL, C., & SCHILTZ, J. (04 April 2022). trajeR, une nouvelle librairie R pour les modèles de mélange pour données longitundinales [Paper presentation]. 8e Journées du GdR Ecologie Statistique, Montpellier, France.

NOEL, C., & SCHILTZ, J. (2022). trajeR, an R package for cluster analysis of time series. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/50962.

NOEL, C., & SCHILTZ, J. (02 June 2021). Multiple Trajectory Analysis in Finite Mixture Modeling [Paper presentation]. The 19th Conference of the Applied Stochastic Models and Data Analysis International Society ASMDA2021, Athens, Greece.

NOEL, C., & SCHILTZ, J. (2021). trajeR - une nouvelle librairie R pour les modèles de mélanges pour données longitudinales. In CNRIUT' 2021 - Recueil des Publications (https://cnriut2021.sciencesconf.org/data/pages/CNRIUT2021_LYON.pdf). France: Assemblée des directeurs d'IUT.
Peer reviewed

NOEL, C., & SCHILTZ, J. (04 June 2020). TrajeR an R package for the clustering of longitudinal data [Paper presentation]. SMTDA 2020, Barcelona, Spain.

NOEL, C., & SCHILTZ, J. (04 June 2020). Identifiability of Finite Mixture Models [Paper presentation]. SMTDA 2020, Barcelona, Spain.

NOEL, C., & SCHILTZ, J. (2020). Identifiability of Finite Mixture Models with underlying Normal Distribution. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/45317.

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