Sparse STATIS-Dual via Elastic Net

Mathematics

Mitzi Cubilla-Montilla¹  Carmen C. Rodríguez-Martínez¹  Purificación Galindo-Villardón²  Purificación Vicente-Galindo² 

[1] Departamento de Estadística, Universidad de Panamá, Panamá 0824, Panama;Department of Statistics, University of Salamanca, 37008 Salamanca, Spain;

关键词: sparse;    STATIS-dual;    elastic net;    multivariate analysis;    multiway tables;    regularization;   

DOI  :  10.3390/math9172094

来源: DOAJ

【 摘 要 】

Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.

【 授权许可】

Unknown