We construct optimal designs for heteroscedastic models when the goal is to make efficient prediction over a compact interval. It is assumed that the point or points which are interesting to predict are not known before the experiment is run. Two minimax strategies for minimizing the maximum fitted
Minimax d-optimal designs for item response theory models
β Scribed by Martijn P. F. Berger; C. Y. Joy King; Weng Kee Wong
- Publisher
- Springer
- Year
- 2000
- Tongue
- English
- Weight
- 950 KB
- Volume
- 65
- Category
- Article
- ISSN
- 0033-3123
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
We consider the Bayesian D-optimal design problem for exponential growth models with one. two or three parameters. For the one-parameter model conditions on the shape of the density of the prior distribution and on the range of its support are given guaranteeing that a one-point design is also Bayes
In this article we consider D-optimal designs for polynomial regression models with low-degree terms being missed, by applying the theory of canonical moments. It turns out that the optimal design places equal weight on each of the zeros of some Jacobi polynomial when the number of unknown parameter