Starting from Spearman’s 1904 pioneering work on factor analysis, latent variable models have witnessed an ever increasing, even though sometimes controversial, diffusion in the statistical literature. They have been extended to deal with different kinds of data structures, and thereby helped to analyse more and more complex situations. Finally, they turned out to be both a powerful instrument for a better understanding of reality and a necessary tool to perform dimension reduction. With the development of refined latent variable models new computational algorithms have been designed that rendered the corresponding parameter estimation fast and reliable. New research lines have incorporated latent variables as a necessary building block.
Angela, M., Maurizio, V. (2016). Special issue on Advances in latent variables: methods, models and applications. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 10(2), 133-137 [10.1007/s11634-016-0252-z].
Special issue on Advances in latent variables: methods, models and applications
MONTANARI, ANGELA;
2016
Abstract
Starting from Spearman’s 1904 pioneering work on factor analysis, latent variable models have witnessed an ever increasing, even though sometimes controversial, diffusion in the statistical literature. They have been extended to deal with different kinds of data structures, and thereby helped to analyse more and more complex situations. Finally, they turned out to be both a powerful instrument for a better understanding of reality and a necessary tool to perform dimension reduction. With the development of refined latent variable models new computational algorithms have been designed that rendered the corresponding parameter estimation fast and reliable. New research lines have incorporated latent variables as a necessary building block.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
