DIPARTIMENTO DI SCIENZE STATISTICHE
Seminar
MULTIVARIATE SCREENED NORMAL
CLASSIFICATION ANALYSIS
H YOUNG -M OON KIM
Konkuk University, Seoul, Korea
December 9, 2014
14.30
Aula Cucconi
Abstract
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DIPARTIMENTO DI SCIENZE STATISTICHE
UNIVERSITÀ DEGLI STUDI DI PADOVA
MULTIVARIATE SCREENED NORMAL CLASSIFICATION ANALYSIS
HYOUNG-MOON KIM
PROFESSOR AT THE DEPARTMENT OF APPLIED STATISTICS
COLLEGE OF COMMERCE AND ECONOMICS, KONKUK UNIVERSITY, SEOUL, KOREA
Abstract
In many real problems, we encounter the situation of screening. We consider the
multivariate screening scheme where underlying distribution is jointly normal
distribution. Following this assumptions, we derive a new classification rule which
usually outperforms the classical LDA or QDA when underlying joint distribution of
screening random vector and observation vector is truly normal distribution.
Resulted classification rule is nonlinear in observation vector so an approximate
linear classification rule is obtained. Based on this linear classification analysis, we
obtain several aspects of this rule including error rates based on total probability of
misclassification, effect of misspecified screening interval, and comparison with the
classical normal classification. ML estimates via ECM algorithm is clearly derived.
The suggested multivariate screened classification rules are illustrated in detail
using some numerical examples.
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