Functional projection pursuit regression
β Scribed by Ferraty, F.; Goia, A.; Salinelli, E.; Vieu, P.
- Book ID
- 115501172
- Publisher
- CrossRef test prefix
- Year
- 2012
- Tongue
- English
- Weight
- 929 KB
- Volume
- 22
- Category
- Article
- ISSN
- 1234-5678
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Projection Pursuit regression (PPR) approximates a regression function f(X ) by a ΓΏnite sum of ridge functions L l=1 f l ( T l X ). When the explanatory vector X is normally distributed, Johansen and Johnstone (1990) gave an one-term approximation formula to the signiΓΏcance level of a test of H0: f=
The original Self-Organizing Map (SOM) algorithm is known to perform poorly on regression problems due to the occurrence of nonfunctional mappings. Recently, we have introduced an unsupervised learning rule, called the Maximum Entropy learning Rule (MER), which performs topographic map formation wit