Optimal classifier for multivariate rectangle-screened normal data classification

Autor:	Young Joo Yoon, Hyoung-Moon Kim, Hea-Jung Kim
Rok vydání:	2015
Předmět:	Statistics and Probability Multivariate statistics Data classification Maximization Bayesian inference computer.software_genre Classification rule Statistics Linear approximation Data mining Rectangle computer Classifier (UML) Mathematics
Zdroj:	Journal of the Korean Statistical Society. 44:498-515
ISSN:	1226-3192
DOI:	10.1016/j.jkss.2015.01.004
Popis:	This paper discusses the classification procedures which make provision for the case where the interest of an investigator is to classify a multidimensional screened normal observation into one of two or more populations. A number of problems are thoroughly investigated in order to render the procedures optimal. These include multidimensional screened normal models that describe the evolutions of the screened observations from different populations, derivation of an optimal classification rule and its linear approximation, and estimation of the rules via the expectation–maximization algorithm with a sequence of conditional maximization steps. The efficiency of the classification rules is examined by using simulation studies. Using real data of bank employee’s salary from the IBM SPSS Statistics 19, we illustrate the empirical relevance of the screened classification.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8a921b224fdffeef7352624c9db951a8 https://doi.org/10.1016/j.jkss.2015.01.004 Zobrazit plný text záznamu Full text from SpringerLink