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...

Nonlinear mixed-effects (NLME) models and generalized linear mixed models (GLMM) are popular in the analysis of longitudinal data and clustered data. Covariates are often introduced to partially explain the large between individual (cluster) variation. Many of these covariates, however, contain missing data and/or are measured with errors [1]. In these cases, likelihood inference can be computa...

The femR315 gene was recently identified by Tn551 insertional mutagenesis as one of the new auxiliary genes, the alteration of which resulted in a drastically reduced methicillin resistance of the Staphylococcus aureus strain COL. femR315 (also known as femD) theoretically encoded a protein of 451 amino acids showing significant amino acid sequence homology with phosphoglucomutases and similar ...

Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but this paper argues that mixed-effects (multilevel) models provide a better alternative method. First, models are discussed in which the two random factors of participants and items are crossed, and not nested. Traditional ANOVAs are compared against these crossed mixed-effects models, for simulated ...

Social network analysis (SNA) has recently emerged as a fundamental tool to study animal behavior. While many studies have analyzed the relationship between environmental factors and behavior across large, complex populations, few focused on species living in small groups due limitations of statistical methods currently employed. Some difficulties are often comparing social structure different ...

Generalized linear mixed models (GLMMs) are useful for modelling longitudinal and clustered data, but parameter estimation is very challenging because the likelihood may involve high-dimensional integrals that are analytically intractable. GaussHermite quadrature (GHQ) approximation can be applied but is only suitable for low-dimensional random effects. Based on the Quasi-Monte Carlo (QMC) appr...

The multiple repairable systems may present system-to-system variability owing to the factors such as operation environments and working intensities of individual systems. It may be more reasonable to assume a heterogeneity among all the systems. In this paper, we propose a nonlinear mixed-effects model (NLMM), called power law process-nonlinear mixed-effects model (PLP-NLMM) and log linear pro...

The paper discusses asymptotic properties of penalized spline smoothing if the spline basis increases with the sample size. The proof is provided in a generalized smoothing model allowing for non-normal responses. The results are extended in two ways. First, assuming the spline coefficients to be a priori normally distributed links the smoothing framework to generalized linear mixed models (GLM...