Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data

Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Generalized Linear Mixed Models for Nominal Data

Nominal variables include unordered polytomous variables and permutations. An unordered polytomous response is one among a set of categories whereas a permutation is an ordering of categories. The categories are nominal in the sense that they do not possess an inherent ordering shared by all units as is assumed for ordinal variables. Using decision terminology, we will refer to the categories a...

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...

Practical likelihood analysis for Spatial Generalized Linear Mixed Models

We propose a standard approach to make inference for spatial generalized linear mixed models using Laplace approximation. Based on analysis of two datasets previous analysed in literature, we compare our approach with different approaches. The first the rhizoctonia root rot dataset is an example of Binomial SGLMM and the second rongelap dataset is an example of Poisson or Negative Binomial SGLM...