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We used a multivariate error correction method described by Wood et al (19) in the Fibrinogen Studies Collaboration.
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To formulate the causal relationships, we used a multivariate probit model.
The Bayesian kriging model assumes that spatial errors are modeled using a multivariate Gaussian distribution with covariance matrix expressed as a parametric function of the distance between pairs of points.
Hierarchical clustering analyses were performed for the differentially expressed genes for all the methods of adjustment of Type-I error and cut-off of P-values, using a multivariate unsupervised method, taking into account the relationship between gene expressions.
saplings were analysed using a multivariate method.
4) Statistical classification using a multivariate Gaussian classifier.
Independent risk factors were analysed using a multivariate logistic regression.
Extraction conditions were optimized using a multivariate approach.
A very recent development is multivariate MPS in which an ensemble of variables can be simulated simultaneously using a multivariate TI.
Analyses of independent predictors for survival from univariate analysis were performed using a multivariate logistic regression.
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