6 citations · 7 across the 7 of their papers we have counts for
6 papers · 1 filter
Stochastic EM Estimation and Inference for Zero-Inflated Beta-Binomial Mixed Models for Longitudinal Count Data
John Barrera, Ana Arribas-Gil, Dae-Jin Lee +1
Analyzing overdispersed, zero-inflated, longitudinal count data poses significant modeling and computational challenges, which standard count models (e.g., Poisson or negative bino…
A stochastic method to estimate a zero-inflated two-part mixed model for human microbiome data
John Barrera, Cristian Meza, Ana Arribas-Gil
Human microbiome studies based on genetic sequencing techniques produce compositional longitudinal data of the relative abundances of microbial taxa over time, allowing to understa…
Visualizing Outliers in High Dimensional Functional Data for Task fMRI data exploration
Yasser Aleman-Gomez, Ana Arribas-Gil, Manuel Desco +2
Task-based functional magnetic resonance imaging (task fMRI) is a non-invasive technique that allows identifying brain regions whose activity changes when individuals are asked to…
Bayesian Regression Analysis of Data with Random Effects Covariates from Nonlinear Longitudinal Measurements
Rolando De la Cruz, Cristian Meza, Ana Arribas-Gil +1
Joint models for a wide class of response variables and longitudinal measurements consist on a mixed-effects model to fit longitudinal trajectories whose random effects enter as co…
Pairwise Dynamic Time Warping for Event Data
Ana Arribas-Gil, Hans-Georg Müller
We introduce a new version of dynamic time warping for samples of observed event times that are modeled as time-warped intensity processes. Our approach is devel- oped within a fra…
Lasso-type estimators for Semiparametric Nonlinear Mixed-Effects Models Estimation
Ana Arribas-Gil, Karine Bertin, Cristian Meza +1
Parametric nonlinear mixed effects models (NLMEs) are now widely used in biometrical studies, especially in pharmacokinetics research and HIV dynamics models, due to, among other a…