A robust nonlinear mixed-effects model for COVID-19 death data

Outlier robust nonlinear mixed model estimation.

In standard analyses of data well-modeled by a nonlinear mixed model, an aberrant observation, either within a cluster, or an entire cluster itself, can greatly distort parameter estimates and subsequent standard errors. Consequently, inferences about the parameters are misleading. This paper proposes an outlier robust method based on linearization to estimate fixed effects parameters and varia...

Nonlinear mixed effects models for longitudinal data

Nonlinear Mixed Effects Model for Swine Growth

Growth curves are often fit to serial live weight data typically every 14-21 days from 50 lbs. to 250 lbs. live weight. This serial data usually has underlying relationships or correlations amongst the serial live weight observations. Heavier pigs at birth and weaning usually have a competitive advantage and remain heavier than the remaining pigs of the group. Also, the variation amongst the pi...

Model Building for Nonlinear Mixed-Effects Models

Nonlinear mixed-effects models involve both fixed effects and random effects. Model building for nonlinear mixed-effects models is the process of determining the characteristics of both the fixed and the random effects so as to give an adequate but parsimonious model. We describe procedures based on information criterion statistics for comparing different structures of the random effects compon...

A robust mixed linear model analysis for longitudinal data.

This paper describes robust procedures for estimating parameters of a mixed effects linear model as applied to longitudinal data. In addition to fixed regression parameters, the model incorporates random subject effects to accommodate between-subjects variability and autocorrelation for within-subject variability. Robust empirical Bayesian estimation of subject effects is briefly discussed. As ...