Permutation test for incomplete paired data with application to cDNA microarray data
β Scribed by Donghyeon Yu; Johan Lim; Feng Liang; Kyunga Kim; Byung Soo Kim; Woncheol Jang
- Book ID
- 113557698
- Publisher
- Elsevier Science
- Year
- 2012
- Tongue
- English
- Weight
- 290 KB
- Volume
- 56
- Category
- Article
- ISSN
- 0167-9473
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract A modification of the numbers of degrees of freedom which makes the __F__ ratio test of the equality of two variances applicable also in the paired case with incomplete data is suggested. Monte Carlo simulation studies indicate that the suggested test is reasonably powerful in many case
## Abstract Cluster analysis is a helpful tool for explorative analysis of large and complex data. Most clustering methods will, however, find clusters also in random data. An important aspect of cluster analysis is therefore to distinguish real and artificial clusters, as this will make interpreta