You can view the full text of this article for free using the link below.

Title: Evaluating information criteria in latent class analysis: application to identify classes of breast cancer dataset

Authors: Abdallah Abarda; Mohamed Dakkon; Khawla Asmi; Youssef Bentaleb

Addresses: Laboratoire de Modélisation Mathématiques et de Calculs Economiques, FSJES, Université Hassan 1er, Settat, Morocco ' Département de Statistique et Informatique de Gestion, FSJES, Université Abdelmalek Essaadi, Tetouan, Morocco ' LRIT, Associated Unit to CNRST (URAC No 29), Rabat IT Center – Faculty of Sciences, Mohammed V University in Rabat, Morocco ' EECOMAS-Lab, Ibn Tofail University, Kenitra, Morocco

Abstract: In recent studies, latent class analysis (LCA) modelling has been proposed as a convenient alternative to standard classification methods. It has become a popular tool for clustering respondents into homogeneous subgroups based on their responses on a set of categorical variables. The absence of a common accepted statistical indicator for deciding the number of classes in the study of population represents one of the major unresolved issues in the application of the LCA. Determining the number of classes constituting the profiles of a given population is often done by using the likelihood ratio test, however the use of such methodology is not correct theoretically. To overcome this problem, we propose an alternative for the classical latent class models selection methods based on the information criteria. This article aims to investigate the performance of information criteria for selecting the latent class analysis models. Nine information criteria are compared under various sample sizes and model dimensionality. We propose also an application of ICs to select the best model of breast cancer dataset.

Keywords: latent class analysis; model selection; information criteria; classification methods.

DOI: 10.1504/IJDATS.2021.10037316

International Journal of Data Analysis Techniques and Strategies, 2021 Vol.13 No.1/2, pp.72 - 87

Received: 22 Oct 2018
Accepted: 03 May 2019

Published online: 23 Apr 2021 *

Full-text access for editors Access for subscribers Free access Comment on this article