We applied multilevel models to robustly examine differences in risk of late-stage cancer diagnosis across age groups (defined as age 65+ or < 65), focusing specifically on the effects of state regulatory factors and their interactions with age.
Limma uses linear models to robustly assess the differential expression of genes.
Thus in the first step we use a simpler model to more robustly cluster tracked features into planes, followed by a second step to reconstruct and merge/split groups of planes and create the final object model.
In light of this, the development of in vivo and especially in vitro models to more robustly evaluate DNA damage is necessary.
Furthermore we show that spatial simulations using the Subcellular Element Model are able to robustly reproduce the universal histogram.
Although these approaches are still at risk of being misled by unknown confounders and measurement errors, in contrast to MR, adding more meaningful observed variables to the model may help to robustly handle unaccounted-for factors or high correlations among variables.
By applying this modeling method, one can employ well-understood linear model predictive control techniques to robustly control nonlinear systems.
GMMs are used to robustly model individuals' behavioral patterns.
The proposed method allows one to robustly model the failure time data with an informative multivariate auxiliary covariate.
This indicates that a non-spatial model is not able to robustly describe the histogram of CNN, and that spatial correlations must be accounted for in describing tissue with locally coherent dynamics.
