This article discusses a modelling framework that links two well-known statistical methods: structural equation modelling (SEM) and latent class or finite mixture modelling. This hybrid approach was proposed independently by Arminger and Stein [1], Dolan and Van der Maas [4], and Jedidi, Jagpal and DeSarbo [5]. Here, we refer to this approach as mixture SEM or latent class SEM. There are two di...

We propose an alternative method of conducting exploratory latent class analysis that utilizes latent class factor models, and compare it to the more traditional approach based on latent class cluster models. We show that when formulated in terms of R mutually independent, dichotomous latent factors, the LC factor model has the same number of distinct parameters as an LC cluster model with R+1 ...

This paper proposes a novel language modeling approach called latent word recurrent neural network language model, which solves the problems present in both recurrent neural network language models (RNNLMs) and latent word language models (LWLMs). The proposed model has a soft class structure based on a latent variable space as well as LWLM, where the latent variable space is modeled using RNNL...

Latent class models provide a useful framework for clustering observations based on several features. Application of latent class methodology to correlated, high-dimensional ordinal data poses many challenges. Unconstrained analyses may not result in an estimable model. Thus, information contained in ordinal variables may not be fully exploited by researchers. We develop a penalized latent clas...

This paper proposes a general latent variable approach to discrete-time survival analysis of non-repeatable events such as onset of drug use. It is shown how the survival analysis can be formulated as a generalized latent class analysis of event history indicators. The latent class analysis can use covariates and can be combined with the joint modeling of other outcomes such as repeated measure...

Abstract Latent class analysis (LCA) for categorical data is a model-based clustering and classification technique applied in a wide range of fields including the social sciences, machine learning, psychiatry, public health, and epidemiology. Its central assumption is conditional independence of the indicators given the latent class, i.e. “local independence”; violations can appear as model mis...

Latent class models (LCM) represent the high dimensional data in a smaller dimensional space in terms of latent variables. They are able to automatically discover the patterns from the data. We present a topographic version of two LCMs for collaborative filtering and apply the models to a large collection of user ratings for films. Latent classes are topologically organized on a “star-like” str...

A mixture model approach is developed that simultaneously estimates the posterior membership probabilities of observations to a number of unobservable groups or latent classes, and the parameters of a generalized linear model which relates the observations, distributed according to some member of the exponential family, to a set of specified covariates within each Class. We demonstrate how this...