๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Interval estimator generation with constraint on the systematic error

โœ Scribed by N. G. Nazarov; N. T. Krushnyak

Publisher
Springer US
Year
2006
Tongue
English
Weight
135 KB
Volume
49
Category
Article
ISSN
0543-1972

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Interval constraint propagation with app
Interval constraint propagation with application to bounded-error estimation
โœ Luc Jaulin ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 157 KB

For a large class of bounded-error estimation problems, the posterior feasible set S for the parameters can be de"ned by nonlinear inequalities. The set inversion approach combines classical interval analysis with branch-and-bound algorithms to characterize S. Unfortunately, as bisections have to be

On improved interval estimation for the
On improved interval estimation for the generalized variance
โœ George Iliopoulos; Stavros Kourouklis ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 703 KB

A confidence interval for the generalized variance of a matrix normal distribution with unknown mean is constructed which improves on the usual minimum size (i.e., minimum length or minimum ratio of endpoints) interval based on the sample generalized variance alone in terms of both coverage probabil

On the generalization error by a layered
On the generalization error by a layered statistical model with Bayesian estimation
โœ Sumio Watanabe ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 287 KB ๐Ÿ‘ 1 views

When a statistical estimation is carried out with a model that has layered parameters such as a neural network, the behavior of the generalization error and the optimum model design method are unknown, since unlike the regular model, the asymptotic behavior of the estimated parameters is not clear.