In this simulation study, a discrete-time identification approach based on subspace methods is applied in order to estimate a nominal MIMO state-space model around a given operating point, by probing the system in open-loop with multi-level random signals.
In other words, the AC[2] (i.e. AC_VI) in the AM algorithm is no longer a FIFO queue, but a multi-level random early detection (M-RED) queue, where each I/P/B packet follows a specific RED-like packet mapping function, i.e., this algorithm modified the random early 'drop' concept into random early 'downward mapping' to the lower-priority AC queues.
In this study, parsimonization of the final model (developed for each outcome through the two-step variable selection outlined above) was pursued, though always preserving the multi-level random effects part of the model.
Section 4 describes the survey data, the construction of variables, and our multi-level random effects analytical strategy.
Multi-level random effects modelling was used with day of week as a random effect to adjust for clustering by day of week.
†Odds are derived from multi-level random effects models, adjusted for area level deprivation, age, sex, marital status, education, and social class.
To properly accommodate the multiple observations (i.e., sex partners) from a single respondent, all models were multi-level random effects logistic regression models (STATA version 10.0, xtlogit, random effects) [ 55].
Daily step counts will be analysed using a multi-level random effect regression model allowing for clustering at household level, to compare participants receiving the pedometer and accelerometer based intervention with those receiving usual care.
The results from the different multi-level random-coefficients regressions models are shown in Table 5.
Cross-sectional observational study using multi-level random-coefficient analyses of a representative sample of 357 patients diagnosed with CHF from 42 primary care practices in the Netherlands.
Using restricted maximum likelihood estimation in the function rma.mv, we ran multi-level random-effects (MLRE) models without moderators for each data set that had five or more effect sizes derived from three or more articles.
