๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Design of experiments with unknown parameters in variance

โœ Scribed by Valerii V. Fedorov; Robert C. Gagnon; Sergei L. Leonov


Publisher
John Wiley and Sons
Year
2002
Tongue
English
Weight
162 KB
Volume
18
Category
Article
ISSN
1524-1904

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Variance regression models in experiment
โœ P. A. Barbetta; J. L. D. Ribeiro; R. W. Samohyl ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 168 KB

Variance models are highly important in developing robust products and processes. These models can be employed in process robustness studies through the use of response surface methodology. In most of the applications the models are constructed in terms of the logarithm of the sample variance or the

Testing homogeneity of intra-run varianc
โœ Qi Zeng; Marie Davidian ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 235 KB

A common assumption in the analysis of immunoassay data is a similar pattern of within-run variation across runs of the assay. One makes this assumption without formal investigation of its validity, despite the widely acknowledged fact that accurate understanding of intra-run variation is critical t

Optimal Design of Experiments for Parame
โœ F. Zhang; M. Mangold; A. Kienle ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 248 KB

## Abstract Six different experimental schemes for mass transfer through porous membranes are compared for the efficiency with respect to parameter identification, namely dynamic singleโ€gas permeation, dynamic multiโ€gas permeation, steadyโ€state singleโ€gas permeation, steadyโ€state multiโ€gas permeati

Optimizing the parameters of multilayere
โœ M. S. Packianather; P. R. Drake; H. Rowlands ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 252 KB ๐Ÿ‘ 1 views

The size and training parameters of artificial neural networks have a critical effect on their performance. This paper presents the application of the Taguchi Design of Experiments (DoEs) off-line quality control method in the optimization of the design parameters of a neural network. Being a 'paral