Robust estimation and its application to a classification problem

Henryk Gacki, and Agnieszka Kulawik. "Robust estimation and its application to a classification problem." Mathematica Applicanda 47.2 (2019): null. <http://eudml.org/doc/295489>.

@article{HenrykGacki2019,
abstract = {In the article, a classification problem with two distributed classes is considered. The problem is solving using empirical discriminant functions for Gaussian classifier and estimators for unknown parameters of multivariate normal distribution. The three etimators, maximum likelihood estimator, Kulawik-Zontek estimator and minimum covariance determinant estimator, are compared in two different empirical examples (small size sample and large size sample).},
author = {Henryk Gacki, Agnieszka Kulawik},
journal = {Mathematica Applicanda},
keywords = {Gaussian classifier, Huber’s function, estimator, multivariate normal model},
language = {eng},
number = {2},
pages = {null},
title = {Robust estimation and its application to a classification problem},
url = {http://eudml.org/doc/295489},
volume = {47},
year = {2019},
}

TY - JOUR
AU - Henryk Gacki
AU - Agnieszka Kulawik
TI - Robust estimation and its application to a classification problem
JO - Mathematica Applicanda
PY - 2019
VL - 47
IS - 2
SP - null
AB - In the article, a classification problem with two distributed classes is considered. The problem is solving using empirical discriminant functions for Gaussian classifier and estimators for unknown parameters of multivariate normal distribution. The three etimators, maximum likelihood estimator, Kulawik-Zontek estimator and minimum covariance determinant estimator, are compared in two different empirical examples (small size sample and large size sample).
LA - eng
KW - Gaussian classifier, Huber’s function, estimator, multivariate normal model
UR - http://eudml.org/doc/295489
ER -