Discrimination and Classification Using Both Binary and Continuous Variables
作者:
W.J. Krzanowski,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1975)
卷期:
Volume 70,
issue 352
页码: 782-790
ISSN:0162-1459
年代: 1975
DOI:10.1080/01621459.1975.10480303
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The likelihood ratio classification rule is derived from the location model, applicable when the data contains both binary and continuous variables. A method is proposed for estimating the rule in practical situations and assessing its performance. Losses incurred by the estimation procedure are investigated, and use of Fisher's linear discriminant function on such data is studied for the case of known population parameters. Finally, the proposed rule is applied to some data sets, and its performance is compared with that of some other classification rules.
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