Regression and Clustering in Multivariate Response Scenarios
EM algorithm for multivariate one level model with covariates
EM algorithm for multivariate two level model with covariates
Regression and Clustering in Multivariate Response Scenarios
Selecting the best results for multivariate one level model
Selecting the best results for multivariate two level model
Starting values for parameters
Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.