Discrimination and Classification Using Both Binary and Continuous Variables 论文

摘要

Abstract 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.