Paolo Giordani


Titolo Pubblicato in Anno
Weighted least squares for archetypal analysis with missing data BEHAVIORMETRIKA 2024
Fuzzy and Model Based Clustering Methods: Can We Fruitfully Compare Them? Models for data analysis 2023
Mortality forecasting using the four-way CANDECOMP/PARAFAC decomposition SCANDINAVIAN ACTUARIAL JOURNAL 2023
CPclus: Candecomp/Parafac Clustering Model for Three-Way Data JOURNAL OF CLASSIFICATION 2023
A multi-way analysis of similarity patterns in longevity improvements STATISTICAL METHODS & APPLICATIONS 2023
A cohort study on the gender gap in mortality through the Tucker3 model CLADAG 2023 Book of abstracts and short papers 2023
Cluster Validity Measures for Fuzzy Two-Mode Clustering Building Bridges between Soft and Statistical Methodologies for Data Science 2022
A tensor-based approach to cause-of-death mortality modeling ANNALS OF OPERATIONS RESEARCH 2022
An Application of the Tensor-Based Approach to Mortality Modeling Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022 2022
Clustering models for three-way data Cladag 2021 Book of Abstracts and Short Papers.13th Meeting of the Classification and Data Analysis Group 2021
Random effect models for multivariate mixed data: A Parafac-based finite mixture approach STATISTICAL MODELLING 2021
Bootstrap confidence intervals for principal covariates regression BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2021
A class of two-mode clustering algorithms in a fuzzy setting ECONOMETRICS AND STATISTICS 2020
An Introduction to Clustering with R 2020
Candecomp/Parafac with zero constraints at arbitrary positions in a loading matrix CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020
A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood PLOS ONE 2020
Factor Uniqueness of the Structural Parafac Model PSYCHOMETRIKA 2020
fclust: An R Package for Fuzzy Clustering THE R JOURNAL 2019
A review and proposal of (fuzzy) clustering for nonlinearly separable data INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2019

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