A graphical approach for evaluating and comparing designs for nonlinear models
✍ Scribed by AndréI. Khuri; Juneyoung Lee
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 538 KB
- Volume
- 27
- Category
- Article
- ISSN
- 0167-9473
No coin nor oath required. For personal study only.
✦ Synopsis
A graphical approach is proposed for the purpose of assessing the overall prediction capability of a nonlinear design inside a region of interest R. A visual description is given of the behavior of the meansquared error of prediction throughout the region R. More explicitly, for each of several concentric surfaces inside R, quantile plots of the so-called estimated scaled mean-squared error of prediction (ESMSEP) are obtained. In addition, when the number of control (input) variables in the associated model is equal to one, plots of the maximum and minimum of the scaled mean-squared error of prediction (SMSEP) over a subset of the model's parameter space are developed. The latter plots are primarily used to compare several nonlinear designs. Two examples are presented to demonstrate the usefulness of the proposed plots. (~) 1998 Elsevier Science B.V. All rights reserved.
📜 SIMILAR VOLUMES
Based on a master-curve, mixture properties can be predicted or interpolated at different temperatures and loading times of interest from a limited set of laboratory test data. This paper presents a comparative assessment of three methods used for generating the relaxation modulus (E(t)) master-curv
One focus of research in graphical models is how to learn them from a dataset of sample cases. This learning task can pose unpleasant problems if the dataset to learn from contains imprecise information in the form of sets of alternatives instead of precise values. In this paper we study an approach