Discriminant analysis using mixed continuous, dichotomous, and ordered categorical variables
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摘要This article proposes an intuitive approach for predictive discriminant analysis with mixed continuous, dichotomous, and ordered categorical variables that are defined via an underlying multivariate normal distribution with a threshold specification. The classification rule is based on the comparison of the observed data logarithm probability density functions. To reduce the computational burden, the analysis is conducted in the context of a confirmatory factor analysis model with independent error measurements. Identification of the dichotomous and ordered categorical variables is discussed. Results are obtained by implementations of a Monte Carlo expectation maximization (MCEM) algorithm and a path sampling procedure. Probabilities of misclassification are estimated via the idea of the "jack-knife" method. A real example is given to illustrate the proposed method.
著者Lee SY, Song XY, Lu B
期刊名稱Multivariate Behavioral Research
出版年份2007
月份1
日期1
卷號42
期次4
出版社LAWRENCE ERLBAUM ASSOC INC-TAYLOR & FRANCIS
頁次631 - 645
國際標準期刊號0027-3171
語言英式英語
Web of Science 學科類別Mathematical Methods In Social Sciences; Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS; Psychology; Psychology, Experimental; PSYCHOLOGY, EXPERIMENTAL; Social Sciences, Mathematical Methods; SOCIAL SCIENCES, MATHEMATICAL METHODS; Statistics & Probability; STATISTICS & PROBABILITY

上次更新時間 2022-17-01 於 00:31