Sentence examples for multivariate tests that from inspiring English sources

We demonstrate how to obtain multivariate tests that are robust to covariance heterogeneity and non-normality with estimators of location and scale based on trimming and Winsorizing.

The resulting tests are generally in accord with each other; we focused on Pillai's trace, since it provides multivariate tests that are robust to differences among the error variance-covariance matrices within groups, as well as to departures from the assumption of multivariate normality [24], [25].

Alternative approaches that model similarities among individual sequences using various kernel functions, such as KBAT (45) and SKAT (46), are multivariate tests that combine single-variant test statistics.

It implements three types of multivariate tests that encompass all commonly used association tests for rare variants.

By placing the most confidence on multivariate tests that take into account the non-independence of variables, the strongest results emerged from analyses of the effects of population size with all four measures of taxonomic richness (ischnoceran, amblyceran, overall species richness and genus richness).

The results of multivariate tests revealed that actual disability and headache impact might be influenced by several characteristics rather than by sociodemographic factors.

40 Most studies conducted multivariate statistical tests (that is, with more than one predictor) to adjust the outcomes for factors other than ethnicity.

The rationale behind the CSF is to meet the demand for multivariate test situations that are not predictive in the sense of previous definition of a specific purpose of measuring a certain mental state.

Multivariate testing demonstrated that there is a significant difference in the mean noise exposure levels between the three groups (p < 0.001).

However, all observed effects were in the anticipated direction, and a joint multivariate test showed that this finding had a p-value of 0.012.

The mean cross-count test is a powerful, non-parametric multivariate two-sample test that is applicable to any case where a notion of distance between observations exists.