@article{MAKHILLJEAS2018132417265,
    title = {Modified One-Step M-Estimator with Robust Scale Estimator for
Multivariate Data},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {24},
    pages = {10396-10400},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.10396.10400},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.10396.10400},
    author = {Hameedah,Nor and},
    keywords = {robust,Q,Modified one-step M-estimator,encountered,trimming criterion,multivariate data},
    abstract = {The Modified One-step M-estimator (MOM) is a highly efficient robust estimator for classifying multivariate data. Generally, robust estimators came into existence as a solution to the inability of classical Linear Discriminant Analysis (LDA) to perform optimally in the presence of outliers. Thus, to solve this shortcoming, the robust MOM estimator is integrated with a highly robust scale estimator, Q<sub>n</sub>, in the trimming criterion of MOM. This introduces a new robust approach termed RLDA<sub>MQ</sub> for handling outliers encountered in multivariate data. The results show the superiority of RLDA<sub>MQ</sub> over the classical LDA and previously existing robust method in literature in terms of misclassification error evaluated through simulated data.}
    }