BACKGROUND The low (LF) vs. high (HF) frequency energy ratio, computed from the spectral decomposition of heart beat intervals, has become a major tool in cardiac autonomic system control and sympatho-vagal balance studies. The (statistical) distributions of response variables designed from ratios of two quantities, such as the LF/HF ratio, are likely to non-normal, hence preventing e.g., from ...

In this work, we consider some computational issues related to the minimum mean-squared error (MMSE) prediction of non-Gaussian variables under a spatial generalized linear mixed model (GLMM). This model has been used to model spatial non-Gaussian variables by Diggle et al. (1998) and Zhang (2002), under which MMSE prediction of non-Gaussian variables can be computed. Since the MMSE prediction ...

We introduce a flexible marginal modelling approach for statistical inference for clustered/longitudinal data under minimal assumptions. This estimated estimating equations (EEE) approach is semiparametric and the proposed models are fitted by quasi-likelihood regression, where the unknown marginal means are a function of the fixed-effects linear predictor with unknown smooth link, and variance...

Phosphoglucosamine mutase (GlmM) catalyzes the formation of glucosamine-1-phosphate from glucosamine-6-phosphate, an essential step in the pathway for UDP-N-acetylglucosamine biosynthesis in bacteria. This enzyme must be phosphorylated to be active and acts according to a ping-pong mechanism involving glucosamine-1, 6-diphosphate as an intermediate (L. Jolly, P. Ferrari, D. Blanot, J. van Heije...

We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. ...

Abstract Generalized linear mixed models form a general class of random effects models for discrete and continuous response in the exponential family. Spatial GLMM are an extension of such models that allows us to fit spatial-dependent data. A popular model in this class is the probit-normal model. In this study we develop a novel exact algorithm to estimate a probit spatial generalized linear ...

The primary mode of transmission of Helicobacter pylori, a human pathogen carried by more than half the population worldwide, is still unresolved. Some epidemiological data suggest water as a possible transmission route. H. pylori in the environment transforms into a nonculturable, coccoid form, which frequently results in the failure to detect this bacterium in environmental samples by convent...

The generalized linear mixed models (GLMMs) for clustered data are studied when covariates aremeasured with error. Themost conventional measurement error models are based on either linear mixed models (LMMs) or GLMMs. Even without the measurement error, the frequentist analysis of LMM, and particularly of GLMM, is computationally difficult. On the other hand, Bayesian analysis of LMM and GLMM i...