Generalized Linear Latent Variable Models with Flexible Distribution of Latent Variables
نویسندگان
چکیده
We consider a semi-nonparametric specification for the density of latent variables in Generalized Linear Latent Variable Models (GLLVM). This specification is flexible enough to allow for an asymmetric, multi-modal, heavy or light tailed smooth density. The degree of flexibility required by many applications of GLLVM can be achieved through the semi-nonparametric specification with a finite number of parameters estimated by maximum likelihood. We show by simulations that the estimated latent variables density capture the true one with good degree of accuracy. Thus, a flexible distribution of latent variables is a powerful tool for exploring the adequacy of the GLLVM for real data. This flexibility brings new insights into the behavior of latent variables.
منابع مشابه
Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that rando...
متن کاملUsing multivariate generalized linear latent variable models to measure the difference in event count for stranded marine animals
BACKGROUND AND OBJECTIVES: The classification of marine animals as protected species makes data and information on them to be very important. Therefore, this led to the need to retrieve and understand the data on the event counts for stranded marine animals based on location emergence, number of individuals, behavior, and threats to their presence. Whales are g...
متن کاملDual Model Misspecification in Generalized Linear Models with Error in Variables
We study maximum likelihood estimation of regression parameters in generalized linear models for a binary response with error-prone covariates when the distribution of the error-prone covariate or the link function is misspecified. We revisit the remeasurement method proposed by Huang, Stefanski, and Davidian (2006) for detecting latent-variable model misspecification and examine its operating ...
متن کاملGeneralized Linear Latent Variable Models for time dependent data
Latent variable models are a fundamental tool for the analysis of multivariate data. The importance of such models is due to the crucial role that latent variables play in many fields, e.g. psychological and educational, socioeconomic, biometric, where often constructs are not directly observable. In these contexts, the different nature of the observable variables often causes theoretical and p...
متن کاملApproximate Bayesian inference in spatial GLMM with skew normal latent variables
Spatial generalized linear mixed models are common in applied statistics. Most users are satisfied using a Gaussian distribution for the spatial latent variables in this model, but it is unclear whether the Gaussian assumption holds. Wrong Gaussian assumptions cause bias in parameter estimates and affect the accuracy of spatial predictions. Thus, there is a need for more flexible priors for the...
متن کامل