Latent factor regression models for grouped outcomes

Latent factor regression models for grouped outcomes.

We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set ...

Bayesian latent variable models for median regression on multiple outcomes.

Often a response of interest cannot be measured directly and it is necessary to rely on multiple surrogates, which can be assumed to be conditionally independent given the latent response and observed covariates. Latent response models typically assume that residual densities are Gaussian. This article proposes a Bayesian median regression modeling approach, which avoids parametric assumptions ...

Modeling adverse birth outcomes via latent factor quantile regression

We describe a Bayesian quantile regression model that uses a factor structure for part of the design matrix. This model is particularly useful when the data comprise numerous indicators of underlying latent factors that analysts wish to include as covariates. We apply the model to a study of birth weights, for which the effects of covariates on the lower quantiles of the response distribution a...

Association of Stressful Life Events with Psychological Problems: A Large-Scale Community-Based Study Using Grouped Outcomes Latent Factor Regression with Latent Predictors

Objective The current study is aimed at investigating the association between stressful life events and psychological problems in a large sample of Iranian adults. Method In a cross-sectional large-scale community-based study, 4763 Iranian adults, living in Isfahan, Iran, were investigated. Grouped outcomes latent factor regression on latent predictors was used for modeling the association of...

Bayesian Grouped Horseshoe Regression with Application to Additive Models