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Huang et al. [6] used local existence results of the models considered and additional assumptions to establish the lower semicontinuity of solution mappings for parametric implicit vector equilibrium problems.

The valuation functions were not stable across both sites for most of the models considered and some of the attributes changed their values between the two points of the consumption process.

Drawing from the best properties of the previous models considered and guided by information from the eye-tracking experiment, a series of new models of random menu selection were constructed using ACT-R/PM.

Analysis results suggest that the CorLAS™ model is the most accurate model among the five models considered and the Battelle, BS 7910, API RP579 and R6 models are in general conservative; furthermore, the API RP579 and R6 models are markedly more accurate than the Battelle and BS 7910 models.

A summary of the models considered, and for which AIC was calculated, is provided in Table 5.

Model averaging methods allow parameter estimation that accounts for model uncertainty by averaging over all (or selected) models considered, and weighting each model by its likelihood [ 5].

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Figure 4 shows the details of the model considered and the critical buckling mode shape.

In particular, a model for the failure rate (Exponential and Weibull model considered) and an Arrhenius-exponential model for failure rate dependency on the stress level are assumed.

Abbreviations: ΔAIC, the difference in AIC between the model considered and the most parsimonious model; c, intercept.

These models considered ODEs and also some spatial but not stochastic effects.

In count models, most implementations are specified according to models considered by Zeger and Qaqish.

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