Assessing the Effect of Selection at the Amino Acid Level in Malaria Antigen Sequences Through Bayesian Generalized Linear Models

Abstract

Understanding human sexual behaviors is essential for the effective prevention of sexually transmitted infections. Analysis of longitudinally measured sexual behavioral data, however, is often complicated by zero-inflation of event counts, nonlinear time trend, time-varying covariates, and informative dropouts. Ignoring these complicating factors could undermine the validity of the study findings. In this paper, we put forth a unified joint modeling structure that accommodates these features of the data. Specifically, we propose a pair of simultaneous models for the zero-inflated event counts: Each of these models contains an auto-regressive structure for the accommodation of the effect of recent event history, and a nonparametric component for the modeling of nonlinear time effect. Informative dropout and time varying covariates are modeled explicitly in the process. Model fitting and parameter estimation are carried out in a Bayesian paradigm by the use of a Markov Chain Monte Carlo (MCMC) method. Analytical results showed that adolescent sexual behaviors tended to evolve nonlinearly over time and they were strongly influenced by the day-to-day variations in mood and sexual interests. These findings suggest that adolescent sex is to a large extent driven by intrinsic factors rather than being compelled by circumstances, thus highlighting the need of education on self protective measures against infection risks.

Is age-related decline in lean mass and physical function accelerated by obstructive lung disease or smoking?

“…To examine the sensitivity of the results from the multilevel regression analyses to these missing observations, we used a joint model for the primary outcomes and missing probabilities [28]. Statistical analyses were performed using PASW Statistics 17.0 (SPSS inc. Chicago, IL).…”

Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method

“…The GLM as a generalized and flexible linear regression (Lee and Nelder, 2006) has shown to highly benefit from the Bayesian techniques for efficient predicting the unknown parameters of the model (Antonio et al, 2005, Merl et al, 2008, Scollnik, 2005, Verrall, 2004. Modeling in a Bayesian framework generally provides the opportunity of powerful yet low-cost computation which makes it suitable for high-dimensional data sets, e.g., hydrological data sets (Barbetta et al, 2018, Bolle et al, 2018, Liu and Merwade, 2018, Sikorska and Seibert, 2018.…”

Dental implant proximity to adjacent teeth: A retrospective study

“…highly benefit from the Bayesian techniques for efficient prediction of the model. 12,13 The Bayesian statistical analysis of GLM, also known as BayesGLM, has recently gained popularity in a range of applications and has been applied to complex prediction modeling problems, such as health informatics and applied statistics, with promising results with small data sets that may have separation problems. 14,15 In this study, the BayesGLM model was constructed using the "arm" package…”