Sentence examples for multinomial regression approach from inspiring English sources

The coarsened multinomial regression approach is not without limitations.

It is in this setting that the advantages of the multinomial regression approach will be maximized.

However, the advantage of the multinomial regression approach is generalization to more than two sub-phenotypes in a unified analysis.

With larger, and more clearly refined disease collections, our multinomial regression approach thus provides a powerful approach to detect variants contributing to the phenotype overall, whilst also highlighting those that may be specific to one category of disease.

We demonstrate, by simulation, that the multinomial regression approach has greater power to detect disease association, in the presence of heterogeneity in allelic odds ratios between sub-phenotypes, than do existing methods formulated in a logistic regression framework.

The multinomial regression approach will have greater power to detect pleiotropic loci, contributing the same, or different, effects to each phenotype, than would traditional analysis of each case-control cohort separately.

The Cochran-Mantel-Haenszel test was applied when the statistical model did not fit the assumptions of the multinomial logistic regression approach.

We compared the performance of LPFS to several state-of-the-art methods, such as SVM recursive feature elimination (SVM-RFE) and sparse multinomial logistic regression approach (SMLR).

Their association with SCCHN was estimated using a logistic regression analysis, while a multinomial logistic regression approach was applied to calculate the effect of the selected polymorphisms on SCCHN different sites (oral cavity, oropharynx, hypopharynx and larynx).

Since our LPFS method is an embedded method and simultaneously optimize classification accuracy and the number of selected features, we specifically choose to compare with an existing method with similar strategy, called sparse multinomial logistic regression approach (SMLR).

Different ordered and multinomial logistic regression approaches were considered for sensitivity analyses.