Jenni Niku, David I. Warton, Francis K.C. Hui, and Sara Taskinen (2017). Generalized Linear Latent Variable Models for Multivariate Abundance Data in Ecology. The Journal of Agricultural, Biological and Environmental Statistics.Niku J, Warton, DI, Hui Francis K.C, Taskinen S. 2017. ...
gllvmis an R package for analysing multivariate ecological data with Generalized Linear Latent Variable Models (GLLVM). Estimation is performed using maximum likelihood estimation, together with either variational approximation (VA) or Laplace approximation (LA) method to approximate the marginal likelihood...
Monte Carlo methods/ generalized linear latent variable modelingmulti-group studieslatent common factorspolytomous responsesmulti-sample latent variable analysismaximum likelihood analysisLatent variable modeling is commonly used in behavioral, social, and medical science research. The models used in such ...
Recently, generalized linear latent variable models for categorical, metric, and mixed-type responses estimated via maximum likelihood (ML) have been proposed. Model deviations, such as data contamination, are shown analytically, using the influence function and through a simulation study, to seriously...
This paper proposes a robust estimator for a general class of linear latent variable models (GLLVM) (Moustaki and Knott 2000, Bartholomew and Knott 1999). It is based on a weighted score function that is simple to implement numerically and is made consistent using the basic idea of indirect ...
Generalized Linear Latent Variables Models (GLLVM) enable the modeling of relationships between manifest and latent variables, where the manifest variables are distributed according to a distribution of the exponential family (e.g., binomial or normal) and to the multinomial distribution (for ordinal ...
Generalized linear latent variable models (GLLVMs) are a powerful class of models for understanding the relationships among multiple, correlated responses. Estimation, however, presents a major challenge, as the marginal likelihood does not possess a closed form for nonnormal responses. We propose a ...
Formulating latent growth using an explanatory item response model approach. In this paper, we present a way to extend the Hierarchical Generalized Linear Model (HGLM; Kamata (2001), Raudenbush (1995)) to include the many forms of measurement models available under the formulation known as the...
Generalized linear models are defined by three components: (1) a linear regression equation, (2) a specific error distribution, and (3) a link function which is the transformation that links the predicted values for the dependent variable to the observed values. ...
Nandram B, Chen M (1996) Accelerating Gibbs sampler convergence in generalized linear models via a reparametrization. J Stat Comput Simul 45: 129–144 View Article MathSciNetNandram B, Chen MH. 1996. Reparameterizing the generalized linear model to acceler- ate Gibbs sampler convergence. ...