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The mathematical and statistical tools for parameter estimation and model discrimination in complex kinetic models are briefly reviewed.

Computational results are presented using multiresponse data for the first time to demonstrate the utility of the expected likelihood in efficient model discrimination in a catalyst fouling system.

We present the theoretical background and the numerical procedure for calculating optimum experimental designs for non-linear model discrimination in the presence of constraints.

Buzzi-Ferraris, P. Forzatti, G. Emig, H. Hofmann, Sequential experimental design procedure for model discrimination in the case of multiple responses.

The five intrinsic LH kinetic models were proposed based on two reaction mechanisms, and subjected to the rigorous parameter estimation and model discrimination in order to obtain the appropriate kinetic model.

It has been applied successfully for model discrimination in both linear and non-linear models for biological systems (see, e.g., [ 23]).

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The addition of dichotomous variables for Killip class (C = 0.78), heart rate (C = 0.73), and systolic blood pressure (C = 0.74) – each as defined for the TIMI STEMI risk score – independently enhanced model discrimination for in-hospital mortality.

In addition, to assess model discrimination, increment in C statistic in a model with traditional risk factor (age, race, sex, and MAP) and a selected baseline variable compared with traditional risk factor alone was calculated.

Model discrimination (AUC=0.737 in men, 0.801 in women) did not change significantly as compared to 10-year prediction time interval.

Motivation: Experiment design strategies for biomedical models with the purpose of parameter estimation or model discrimination are in the focus of intense research.

Here we present the sum of squares formalism which is used to relax and solve the optimization problems posed by the various approaches for model discrimination considered in this paper.

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