In particular, we employ a multivariate generalized autoregressive conditionally heteroskedastic (MVGARCH) model involving time-varying settings and multivariate Markov switching autoregressive conditionally heteroskedastic (MVSWARCH) model with regime-switching techniques and compare them with a conventional linear regression-based (LRB) model.
To model the relationship between the various measures of obesity and risk of CVD in a dynamic survival model way 11 an extended Cox regression model involving time-dependent (time-varying) variables 12 15 was used.
(2) For finite-time synchronization research, the model in this paper is more practical, because the network model involves time delays and stochastic perturbations.
Remark 2 The model in this paper is more practical, because the network model involves time delays, stochastic perturbations, and partially known/unknown transition rates, while the models in [11, 12] did not contain time delays and partially known/unknown transition rates.
Models involving time preferences analyze preference change on the basis of the temporal occurrence of the preference alone.
The development of the skin layer is interpreted in the light of a phenomenological model involving two time variables: the time allowed for relaxation of the highly oriented melt until the crystallization temperature is reached and the relaxation time of the material.
The lmer function from the R package « lme4 » was used to determine the effect of the treatment on the dynamics of progeny production by comparing with an ANOVA the Akaike Information Criterion of a model involving both time, treatment and their interaction to that of a null model involving only time.
The lmer function from the R package « lme4 » was used to determine the effect of the treatment on the genotype frequency by comparing with an ANOVA the Akaike Information Criterion of a model involving both time, treatment and their interaction to that of a null model involving only time.
Many approaches involving time series models have been used for traffic forecasting, such as statistical models or models based on neural networks, [1].
Simulations characteristically are used in connection with dynamic models, i.e. models that involve time.
In fact, a recent study of drug-involved probationers involving time-varying models found that involvement in treatment shortly after being placed on probation has a longer term impact on reductions in drug use and rearrest.
