Conditional Association and Unidimensionality in Monotone Latent Variable Models

A Characterization of Monotone Unidimensional Latent Variable Models

A Semiparametric Approach to Mixed Outcome Latent Variable Models: Estimating the Association between Cognition and Regional Brain Volumes

Multivariate data that combine binary, categorical, count and continuous outcomes are common in the social and health sciences. Often, mixed outcome variables together are considered to be tapping a particular latent construct. A common research question then focuses on estimation of the relationship between a latent construct and a scientifically important covariate of interest. A motivating e...

Assessing Unidimensionality Through LISREL: An Explanation and an Example

Research in MIS often focuses on the relationships among latent variables of interest that cannot be directly measured. Because of potential error in measurement and associated confounding, indirect measurement of latent constructs requires formal assessments of reliability and validity. Without these measures, resultant paths in causal implications may be inaccurate, biased, and unstable. Howe...

Isotone additive latent variable models

For manifest variables with additive noise and for a given number of latent variables with an assumed distribution, we propose to nonparametrically estimate the association between latent and manifest variables. Our estimation is a two step procedure: first it employs standard factor analysis to estimate the latent variables as theoretical quantiles of the assumed distribution; second, it emplo...

Spatial Latent Gaussian Models: Application to House Prices Data in Tehran City

Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...