Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and
statistical inference. There has been a recent upsurge in the application of latent class …

We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

This commentary provides a brief mathematical review of exploratory factor analysis, the
common factor model, and principal components analysis. Details and recommendations …

The present guide provides a practical guide to conducting latent profile analysis (LPA) in
the Mplus software system. This guide is intended for researchers familiar with some latent …

Background Missing data are unavoidable in epidemiological research, potentially leading
to bias and loss of precision. Multiple imputation (MI) is widely advocated as an …

Exploratory factor analysis (EFA) is a multivariate statistical method that has become a
fundamental tool in the development and validation of psychological theories and …

Missing data is a problem that occurs frequently in many scientific areas. The most
sophisticated method for dealing with this problem is multiple imputation. Contrary to other …

Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …