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We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels.
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The influence of season, depth and latitude were analysed through multivariate analysis of variance using permutations and distance-based linear models and assemblages were characterised based on multivariate ordination and hierarchical classification methods.
A student's t-test based on multivariate permutation was performed with adjustment for multiple comparisons and with random variance model.
The candidates of the outliers were selected automatically by a jackknife outlier analysis based on multivariate statistics.
The CFR techniques used in both of the two reconstructions are based on multivariate linear regression approaches, which are based on calibrating proxy records against instrumental data12,13,14,15,16,17.
Dang and Serfling [3] introduced nonparametric multivariate outlier identifiers based on multivariate depth functions.
This analysis is based on multivariate logistic regression analyses.
The current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design.
We give illustrative examples based on the multivariate normal and multivariate Student t distributions.
Thus, based on the multivariate delta method, the variance of the percent recovery is given by (AI.4) where is the gradient of.
95% CIs were calculated based on these variance estimates.
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