A generalized Mahalanobis distance for mixed data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:92

Article

de Leon, AR ; Carrière, KC

关键词: latent variable models;    maximum likelihood;    measurement level;    multivariate normal distribution;    polychoric and polyserial correlations;    probit models;   

DOI  :  10.1016/j.jmva.2003.08.006

来源: Elsevier

【 摘 要 】

A distance for mixed nominal, ordinal and continuous data is developed by applying the Kullback-Leibler divergence to the general mixed-data model, an extension of the general location model that allows for ordinal variables to be incorporated in the model. The distance obtained can be considered as a generalization of the Mahalanobis distance to data with a mixture of nominal, ordinal and continuous variables. Moreover, it includes as special cases previous Mahalanobis-type distances developed by Bedrick et al. (Biometrics 56 (2000) 394) and Bar-Hen and Daudin (J. Multivariate Anal. 53 (1995) 332). Asymptotic results regarding the maximum likelihood estimator of the distance are discussed. The results of a simulation study on the level and power of the tests are reported and a real-data example illustrates the method. (C) 2003 Elsevier Inc. All rights reserved.

【 授权许可】

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