## Abstract The article investigates different estimation techniques in the simple linear controlled calibration model and provides different types of confidence limits for the calibration estimator. In particular, Mβestimation and bootstrapping techniques are implemented to obtain estimates of reg
Confidence intervals for calibration with neural networks
β Scribed by M. Dathe; M. Otto
- Book ID
- 113022517
- Publisher
- Springer
- Year
- 1996
- Tongue
- English
- Weight
- 561 KB
- Volume
- 356
- Category
- Article
- ISSN
- 1618-2650
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
This paper discusses the use of supervised neural networks as a metamodeling technique for discreteevent, stochastic simulation. An (s, S ) inventory simulation from the literature is translated into a metamodel through development of parallel neural networks, one estimating expected total cost and
We present the theoretical results about the construction of confidence intervals for a nonlinear regression based on least squares estimation and using the linear Taylor expansion of the nonlinear model output. We stress the assumptions on which these results are based, in order to derive an approp