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For robust inference, a semiparametric multivariate approach is proposed to determine the significance of parametric maps.
The main advantage of this multivariate approach is the high sensitivity to stressors at multiple scales and the universal applicability across ecosystem borders and geographic regions.
In this methodological work, hydrological droughts are considered, and a multivariate approach is proposed, by regarding as relevant variables the duration and the average intensity.
The necessity of using multivariate approach is discussed and a simplified method is proposed to account for directional dependence, which not only provides accurate prediction but also reduces calculation effort.
The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models.
Generally few sensors, sometimes only one, are applied and time series obtained, which are then analysed by means of control cards and/or univariate graphs, but surely there is a correlation between temperature and humidity so a multivariate approach is necessary [8].
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A multivariate approach was used for optimization of the variables involved in the system.
A multivariate approach was adopted as an analytical tool to study the effect of different parameters and to improve spectrophotometric conditions for the analysis of ATR-Ca.
The band patterns generated were transformed into a presence/absence matrix and a multivariate approach was used to test for differences in the locations.
In a first instance, a multivariate approach was used to screen and optimize the UniSpray source settings to maximize detection sensitivity.
In the meantime, we have developed a two step procedure that seems to be quite robust, i.e. the gains from a multivariate approach are expected to be low, as explained below.
More suggestions(15)
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