A Generalization of the GMANOVA-Model
โ Scribed by Dr. H. Hecker
- Publisher
- John Wiley and Sons
- Year
- 1987
- Tongue
- English
- Weight
- 333 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0323-3847
No coin nor oath required. For personal study only.
โฆ Synopsis
It is shown that in some experimental designs the MAXOVAand ths GMAKOVA-model are too restrictive either to yield all hypothesis testa of interest or to reflect all known features of the design. An exbnaion of these models is derived by relating the response vectors with the unknown model parametem by linear equations which may be completely different for each of the p components of the reqonse vector and for each of the n independent vectors. For .situations. in which a Wishartdistributed estimator for the underlying common covariance matrix is attainable, a test for any 8-dimemionel linear hypothesis on the model parameters is derived.
๐ SIMILAR VOLUMES
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.
## Abstract In this paper, we propose a generalization of the expected discounted penalty function and analyze the proposed analytic tool in the framework of the compound binomial model with a general premium rate __c__ (__c__โโโโ^+^) received per period. We derive an explicit expression for this g
The old Kramers' rule is a useful recurrence relation for the calculation ยฒ < k < : of diagonal n, l r n, l matrix elements between hydrogenic wave functions. An improvement to such a relationship, which considers the most general case of nondiagonal ยฒ < k < : n, l r nะ, lะ matrix elements, is calle