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Mrof is the AMLR alignment obtained using the marginal method.
Parameters of the statistical model were estimated by the marginal method of Breslow [ 26].
Table 3 shows the conditional logit results using the marginal method for the 55 respondents who provided complete data.
The differential eigengene network analysis yields two main novel findings that could not have been obtained using a standard marginal method.
It should be noted that any inference using the log-likelihood from the marginal method is potentially misleading, since the likelihood function assumes best and worst choices are made independently.
It demonstrated that although the marginal method of analysis makes potentially unwarranted assumptions about independence of best and worst choices and the nature of the error terms in the utility model, it provides a good approximation to the paired (maxdiff) model analysis, suggesting that the resulting estimates are consistent.
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Bayesian nonparametric inferential procedures based on Markov chain Monte Carlo marginal methods typically yield point estimates in the form of posterior expectations.
MISA [17] improves upon SNP-at-a-time (marginal) methods by modeling phenotype as a function of a multivariate genetic profile and, as a result, provides measures of association adjusted for the remaining markers.
Yi and Cook reported marginal methods for missing longitudinal data from clustered design [ 12].
The overall performance of sMPAS is a ~20% improvement from the best of the conventional marginal methods.
Multilevel and marginal methods address different questions and the choice of method needs to be made according to the research objective.
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