Model building using covariates in nonlinear mixed-effects models

We start the simulation study without additional covariates to compare our estimator in terms of `2 error to a competitor, the estimator of the row-column model 1 with different

We also considered mice: we applied logistic regression with a classical forward selection method, with the BIC calculated on each imputed data set. However, note that there is

In the following, we introduce paired covariate settings and the paired lasso (Sect. 2), classify cancer types based on two transformations of the same molecular data (Sect. 3),

To estimate the support of the parameter vector β 0 in the sparse corruption problem, we study an extension of the Lasso-Zero methodology [Descloux and Sardy, 2018],

Table 1 – MISE of the kernel estimators with bandwidth selected by the Goldenshluger and Lepski method (GL), and with bandwidth selected by cross-validation (CV) of the

In that case, the penalty term being of the same order than the variance over one model, we conclude that the model selection procedure achieves the "expected rate" of order

For this work we have simulated PK profiles using the model and parameters based on the real data to derive phenotypes (PK phenotypes section), on which we have applied

In this study, we propose an extension of the individual-based Greenlab model, based on a bottom-up approach: the growth of each individual plant in a given field can be