Machine learning techniques are becoming indispensable tools for extracting useful information. Among many machine techniques, variable selection is a solution used converting high-dimensional data into simpler while still preserving the characteristics of original data. Variable aims to find best subset variables that produce smallest generalization error; it can also reduce computational comp...

Latent class analysis (LCA) has allowed epidemiologists to overcome the practical constraints faced by traditional diagnostic test evaluation methods, which require both a gold standard and ample numbers of appropriate reference samples. Over past four decades, LCA methods have expanded allow evaluate tests estimate true prevalence using imperfect over variety complex data structures scenarios,...

This paper uses Factored Latent Analysis (FLA) to learn a factorized, segmental representation for observations of tracked objects over time. Factored Latent Analysis is latent class analysis in which the observation space is subdivided and each aspect of the original space is represented by a separate latent class model. One could simply treat these factors as completely independent and ignore...

While latent class (LC) modeling using bias-adjusted stepwise approaches has become widely popular, little is known on how these methods are affected by missing values. Using synthetic data sets, we illustrate under which conditions values introduce biases in the estimates of relationship between membership and auxiliary variables. We apply three-step LC analysis with both modal proportional as...

Latent variable models exist with continuous, categorical, or both types of latent variables. The role of latent variables is to account for systematic patterns in the observed responses. This article has two goals: (a) to establish whether, based on observed responses, it can be decided that an underlying latent variable is continuous or categorical, and (b) to quantify the effect of sample si...

Latent Class Analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical and/or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this paper, a new methodology, multilevel latent class ...

BACKGROUND Risk prediction of atrial fibrillation (AF) is of importance to improve the early diagnosis and treatment of AF. Latent class analysis takes into account the possible existence of classes of individuals each with shared risk factors, and maybe a better method of incorporating the phenotypic heterogeneity underlying AF. METHODS AND FINDINGS Two prospective community-based cohort stu...