✦ LIBER ✦
Discrimination and allocation using a mixture of discrete and continuous variables with some empty states
✍ Scribed by M.A.A Moussa
- Publisher
- Elsevier Science
- Year
- 1980
- Weight
- 430 KB
- Volume
- 12
- Category
- Article
- ISSN
- 0010-468X
No coin nor oath required. For personal study only.
✦ Synopsis
The likelihood ratio classification rule based on the location model is estimated given: (1) data consist of both binary and continuous variables; (2) some states have either zero frequency or too few observations, the case that usually happens in practice. An iterative proportional fitting of a convenient approximation of the log-linear models as well as a linear additive model are utilized in estimating the rule's parameters. Performance of the obtained rule is then assessed by estimated error rates.
Discriminant functions
Error rates Linear additive model Log-linear models