Abstract
We consider the optimal design of experiments in which estimation and design are performed by different parties. The parties are assumed to share similar goals, as reflected by a common loss function, but they may have different prior beliefs. After presenting a few motivating examples, we examine the problem of optimal sample size selection under a normal likelihood with constant cost per observation. We also consider the problem of optimal allocation for given overall sample sizes. We present results under both squared-error loss and a logarithmic utility, paying attention to the differences between one- and two-prior optimal designs. An asymmetric discrepancy measure features repeatedly in our development, and we question the extent of its role in optimal two-prior design.
Original language | English (US) |
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Pages (from-to) | 1404-1411 |
Number of pages | 8 |
Journal | Journal of the American Statistical Association |
Volume | 88 |
Issue number | 424 |
DOIs | |
State | Published - Dec 1993 |
Externally published | Yes |
Keywords
- Adversarial design
- Bayesian analysis
- Conjugate prior
- Optimal allocation
- Posterior distribution
- Predictive distribution
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty