Sentence examples for mixture modelling and from inspiring English sources

SNOB uses probabilistic mixture modelling and the minimum message length principle to determine the optimum class structure (clusters) within the data.

In this paper, a hierarchical mixture regression model [ 28] has been adopted to simultaneously account for the heterogeneity of birth weights (via mixture modelling) and adjust for risk factors and complication variables (via logistic regression).

The phenomenon of expression "bimodality" was reported for other cancers as well [ 20- 22], where «bimodality» was calculated by selection of hypervariable (HV) genes using F-statistics [ 20] and a combination of mixture modelling and kurtosis [ 21, 22].

Hernandez et al [ 22] compare linear and TOBIT models with adjusted censored models (which essentially modify the TOBIT) and similar models in a mixture modelling framework (but none were applied to cancer datasets and QLQ-C30 in particular).

The analysis of longitudinal differences in outcome will use two complementary statistical approaches: multilevel (mixed) modelling (MLM) and growth mixture modelling (GMM) as applied to randomised preventative interventions by Muthén [ 47].

Methods include group-based trajectory modelling [10, 13] and growth mixture modelling (GMM) [11, 12].

After normalisation six cut-off algorithms were compared, these included the naïve cut-off; GENCOM; GACK; Porwollik; Minimum Kernel Density; and Mixture modelling methods, as follows.

Here, some of the more widely used algorithms to determine the cut-off in bacterial CGH studies were compared, along with two novel approaches, the MKD and mixture modelling approaches.

In this study we examined two different models, namely latent class growth analysis (LCGA) and growth mixture modelling (GMM).

Growth curve and growth mixture modelling were used to identify trajectories of HRQoL.

In contrast to the logistic regression approach that works on dichotomous outcomes of large for gestational age, the mixture modelling method attempts to model directly the birth weights, which are more informative relative to dichotomized outcomes for examining effects of risk factors.