Zoom link https://videoconf-colibri.zoom.us/j/95155975049
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Abstract
This presentation introduces a novel methodology to model the hierarchical dependence structure of manifest variables. This is done by reconstructing their correlation matrix considering a hierarchy of latent factors which forms an ultrametric correlation matrix. The proposed Ultrametric Factor Analysis (UFA) model will be shown to obtain reliable, unidimensional, and unique hierarchical factors. This approach addresses the limitations of traditional factor analysis methods that often oversimplify the multidimensional and complex relationships among manifest variables. The paper provides an in-depth mathematical description of the proposed model, as well as an algorithm for parameter estimation. An extensive simulation study with 3, 000 generated samples validates the proposal across twelve different scenarios. Finally, we demonstrate the potential of the proposed model using a real data set that is a benchmark in psychological research.
Short Bio
Maurizio Vichi is a full professor of statistics at Sapienza University of Rome. He has been the founding President of the Federation of European National Statistical Societies (FENStatS). He has been head of the Department of Statistical Sciences at Sapienza, President and Secretary General of the Italian Statistical Society, President of the International Federation of Classification Societies, chair of ESAC the European Statistical Advisory Committee of the European Union, representing the Council.
He is the Editor-in-chief of the International Journal Advances in Data Analysis and Classification.
He is the author of more than 150 papers on: classification and clustering, multivariate methods, and three-way analyses, published in international journals.
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