Effects of different parameter estimators to error rate in discriminant analysis

Özet

Discriminant analysis is defined as a statistical technique that classifies a unit whose properties are measured, into one of the known finite numbers of populations. In this classifying process, an error occurs when the unit is classified to different population from its own population. This error is called the error rate or the probability of incorrect classification. It is desirable to minimize this error. This study focuses on determining the parameter estimation method that provides the minimum error rate, when the parameters of Weibull populations are not known. Maximum likelihood (ML), moments (MOM) and least squares (LS) methods are chosen from among parameter estimation methods. By a conducted simulation study, it is investigated that the error rate how is affected by the ML, LS and MOM estimates.