Sentence examples for multiple response models from inspiring English sources

In addition to handling multiple response models, several features have been integrated into PFIM 3.0 for model specification and optimisation.

In this paper, we present an extended version, PFIM 3.0, dedicated to both design evaluation and optimisation for multiple response models, using a similar method as for single response models.

A central composite design was used to investigate the effect of process variables and a second order multiple response model was used to relate the pressure, temperature and holding time to WHC and GS.

We explore the modifications that are required in order to adapt an EM algorithm with first-order and fully exponential Laplace approximations to a non-nested, multiple response model.

A central composite rotatable design and a second order multiple response model were used to investigate the effect of process variables (pressure, temperature and holding time) and relate them to coagulation parameters.

A set of nondestructive data have been gathered based on general factorial design of experiments and used for training and testing the multiple response surface model.

Considering both uncertainty and correlations among multiple responses from model and physical observations imposes challenges.

We used multiple-group graded response models (Muthén and Asparouhov 2002; Samejima 1969) to test MI of the four-item motivation-to-learn scale.

This paper presents further analyses using multiple-group graded response models to scrutinize the validity of the motivation-to-learn scale for group comparisons.

Furthermore, we used multiple-group graded response models to evaluate the degree of measurement invariance across gender, age groups, level of education, and migration background using PIAAC data.

In order to fit multiple-group graded response models in Mplus and test the degree of MI, the same number of categories must be present in all groups.