Sentence examples for multivariate response models from inspiring English sources

In this paper we present a program, Popdes, to investigate the D-optimal design of individual and population multivariate response models, such as pharmacokinetic pharmacodynamic, physiologically based pharmacokinetic, and parent drug and metabolites models.

Tests were made for associations within each functional domain by exploring a linear multivariate response model.

Table 3 shows the random part of the multivariate response model.

A multivariate response model was constructed to answer our research questions.

It is worth noting from the multivariate response model that the socioeconomic variables of interest had stronger associations with physical functioning (accounting for 24% of variation) than psychological well-being (accounting for 7% of total variation).

We developed a 2-level model of 17070 (two outcomes for each individual) at level 1 nested within 8535 individuals at level 2. By treating multiple outcomes within the multivariate response model, we were able to estimate the covariance between two outcomes nested within individuals, as well as the variance for each outcome in a simultaneous manner.

This study presents a genetic algorithm (GA) for identifying the exact D-optimal design for multivariate response surface models (called MD-optimal design).

The volumetric standard states as well as the interaction coefficients were determined from combined use of a multivariate response surface model together with the McMillan Mayer formalism and a 3rd-order virial expansion, without relying on either pure component properties or binary/ternary solution information.

A Bayesian decision-theoretic method identified those morphological features, which best explained neuropsychological test scores in the context of a multivariate response linear model with interactions.

However, estimation of mixed models for multivariate response of dimension four or larger is computationally not feasible.

While these could be treated as a multivariate response, we will analyze three separate models to be consistent with the dimension reduction procedures in "Dimension reduction techniques" section, which were developed assuming a univariate response.