@article{article_333936, title={Effects of different parameter estimators to error rate in discriminant analysis}, journal={Sakarya University Journal of Science}, volume={22}, pages={1609–1616}, year={2018}, DOI={10.16984/saufenbilder.333936}, author={Demirci Biçer, Hayrinisa and Biçer, Cenker}, keywords={optimal classification,Weibull distribution,error rate,discriminant analysis}, abstract={<span style="font-size:12pt;font-family:’Times New Roman’, serif;">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. How the error rate is affected by parameter estimators ML,
MOM and LS is examined with the simulation study. </span>}, number={6}, publisher={Sakarya University}