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For each time t, the p values are obtained by fitting a multivariate skew t-distribution with (first model) and without a symmetry constraint (second model) to the (t - 120) historical market returns.
The prognostic role of HER2 status on EFS and OS was assessed by fitting a multivariate proportional hazard model to the data.
This sum of absolute differences was estimated by fitting a multivariate regression model and using an LRT on 20° of freedom to test the overall difference between cases and controls over the hormone vector.
For these reasons, we ultimately took a Bayesian approach by fitting a multivariate negative binomial reduced rank random regression model using a Metropolis Hastings algorithm implemented in R [ 17].
A PS model to predict the probability that a given patient would be admitted to the ICU at his first triage, conditional on baseline-measured covariates, was obtained by fitting a multivariate logistic model [ 6].
We then applied an a priori defined statistical protocol by fitting a multivariate logistic regression model that included all variables that were significantly associated with compulsory drug detention experience at the p ≤ 0.05 level in univariate analyses.
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This could also be achieved by fitting a sire-dam multivariate BLUP model using cumulative records during the laying period from which breeding values for fertility of both sexes can be obtained for each candidate for selection.
Multivariate analysis was performed by fitting a Cox proportional hazard model to the data.
After univariate analyses, multivariate linear regression analyses were performed by fitting a model with all variables and all two-factor interactions.
The discriminant fits a multivariate normal density to each data group, with a pooled estimate of covariance defined by (12).
The variables that received a non-negative coefficient after the Lasso fit were then used to fit a multivariate linear model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com