& Yan, P. (2005), `Generalized linear latent variable models for repeated measures of spatially correlated multivariate data', Biometrics 61(3), 674-683.Zhu J., Eickhoff J.C., Yan P.: Generalized linear latent
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...
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...
Latent variable models represent a useful tool for the analysis of complex data when the constructs of interest are not observable. A problem related to these models is that the integrals involved in the likelihood function cannot be solved analytically. We propose a computational approach, referred...
Nandram B, Chen M-H. 1996. Reparameterizing the Generalized linear model to acceler- ate Gibbs sampler convergence. Journal of Statistical Computation and Simulation 54(1- 3): 129-144.Nandram B, Chen M (1996) Accelerating Gibbs sampler convergence in generalized linear models via a re...
Simulating Generalized Linear Models 6 6.1 INTRODUCTION In the previous chapter, we dug much deeper into simulations, choosing to focus on the standard linear model for all the reasons we discussed. However, most social scientists study processes that do not conform to the assumptions of OLS. ...
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. ...
[2]Kidzinski, L., Hui, F.K.C., Warton, D.I., Hastie, T.J. (2022). Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays. Journal of Machine Learning Research, 23(291): 1--29. ...
That is, could there be a class of vector generalized linear mixed models (VGLMMs)? The answer to this second good question is that adding random effects to the VGLM class would be a very useful feature to have so that VGLMMs are certainly possible. However, it would take much work to...
An R package for estimating generalized additive mixed models with latent variables latent-variable-modelshierarchical-modelsgeneralized-additive-modelsitem-response-theorystructural-equation-models UpdatedSep 22, 2024 C++ Fit, evaluate, and visualise generalised additive models (GAMs) in native Julia ...