Abstract
In this paper some new properties and computational tools for finding KL-optimum designs are provided. KL-optimality is a general criterion useful to select the best experimental conditions to discriminate between statistical models. A KL-optimum design is obtained from a minimax optimization problem, which is defined on a infinite-dimensional space. In particular, continuity of the KL-optimality criterion is proved under mild conditions; as a consequence, the first-order algorithm converges to the set of KL-optimum designs for a large class of models. It is also shown that KL-optimum designs are invariant to any scale-position transformation. Some examples are given and discussed, together with some practical implications for numerical computation purposes.
| Original language | English |
|---|---|
| Pages (from-to) | 107-117 |
| Number of pages | 11 |
| Journal | Statistics and Computing |
| Volume | 26 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 1 Jan 2016 |
Keywords
- Continuity
- Convexity
- Discrimination
- Generalized linear models
- Infinite-dimensional spaces
- Invariance
- KL-optimality
- Optimum design
- Regular designs
- Weak convergence metric
Fingerprint
Dive into the research topics of 'KL-optimum designs: theoretical properties and practical computation'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver