Discriminant Analysis Using Mixed Continuous, Dichotomous, and Ordered Categorical Variables

Autor/inn/en	Lee, Sik-Yum; Song, Xin-Yuan; Lu, Bin
Titel	Discriminant Analysis Using Mixed Continuous, Dichotomous, and Ordered Categorical Variables
Quelle	In: Multivariate Behavioral Research, 42 (2007) 4, S.631-645 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0027-3171
Schlagwörter	Factor Analysis; Discriminant Analysis; Probability; Monte Carlo Methods; Numbers; Error of Measurement; Mathematics; Evaluation + Suchen Sie Ihr Suchwort? Faktorenanalyse; Diskriminanzanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Monte-Carlo-Methode; Zahlenraum; Messfehler; Mathematik; Evaluierung
Abstract	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 "jackknife" method. A real example is given to illustrate the proposed method. (Contains 2 figures and 2 tables.) (As Provided).
Anmerkungen	Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10