Variational approximation methods have become a mainstay of contemporary Machine Learning methodology, but currently have little presence in Statistics. We devise an effective variational approximation strategy for fitting generalized linear mixed models (GLMM) appropriate for grouped data. It involves Gaussian approximation to the distributions of random effects vectors, conditional on the res...

The function of UreC, the product of a 1,335-bp-long open reading frame upstream from the urease structural genes (ureAB) of Helicobacter pylori, was investigated. We present data showing that the ureC gene product is a phosphoglucosamine mutase. D. Mengin-Lecreulx and J. van Heijenoort (J. Biol. Chem. 271:32-39, 1996) observed that UreC is similar (43% identity) to the GlmM protein of Escheric...

Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses. Instead of such conventional approaches, statisticians have started proposing better techniques, such as the Generalized Estimating Equations (GEE) approach and Generalized Linear Mixed Models (GLMM) ...

A novel reverse primer (GLM MR1) was designed for detection of the glmM gene in Helicobacter pylori by PCR. The percentage of amplification in clinical isolates using GLM MR1 was 100% for detection of the glmM gene and 86.36% for the ureA gene. The primer designed is useful for the identification of H. pylori.

Helicobacter pylori (Hp) is one of the most important human pathogens that can cause duodenal and gastric ulcers, gastritis and stomach cancer. Hp infection is considered to be a cause of limiting access to bariatric surgery. The aim of this study was to determine the prevalence of Hp in patients with obesity going into bariatric surgery and to reveal the relationship between Hp and clinical da...

BACKGROUND Analyses of multicenter studies often need to account for center clustering to ensure valid inference. For binary outcomes, it is particularly challenging to properly adjust for center when the number of centers or total sample size is small, or when there are few events per center. Our objective was to evaluate the performance of generalized estimating equation (GEE) log-binomial an...

Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability effects resulting mainly from illumination changes. In this paper, we further ge...

In this chapter, we examine the use of special forms of correlated random e ects in the generalized linear mixed model (GLMM) setting. A special feature of our GLMM is the inclusion of random residual e ects to account for lack of t due to extra variation, outliers and other unexplained sources of variation. For random e ects, we consider, in particular, the correlation structure and improper p...