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In the linear regression model, the marginal effects are generally equivalent to the estimated partial slope parameters.

Results show that Gaussian Copula is excellent to model the marginal function of each speed series, and it is reasonable.

For dummy explanatory variables in the binary regression model, the marginal effects are given by the differences in the probabilities when the variable assumes the value of 1 and when it assumes the value of 0 [41].

Pressure time histories obtained at roof locations for buildings of varying roof slope at several angles of incidence in the boundary layer wind tunnel at Clemson University are used to characterize the correlation statistics between tap locations and model the marginal probability density function at individual tap locations.

From each model, the marginal likelihood was estimated using the harmonic mean estimator [59] and the Bayes factor found by taking the ratio.

One knot, placed at the median of the failure time distribution, seems to be sufficient to model the marginal survival function, see Figure 3.

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A high level of agreement was observed between results from the paired model (which is theoretically consistent with the 'maxdiff' choice model) and the marginal model (which is only an approximation to it).

In the presence of competing risks, the WLW model models both the marginal hazard for death and the cause-specific hazard for recurrences [ 19].

In such models, the marginal effects represent the effect of a change in a covariate on the predicted probability of full immunization.

For non-linear models, the marginal model describes the population parameter, while the mixed model describes an individual's [[ 15], p.135].

In a mixture of random effects models, the marginal log-likelihood after integrating out the random effects would be considered.

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