The effect of across-location heteroscedasticity on the classification of mixed categorical and continuous data

Authors:
Chi-Ying Leung
Affiliations:
Department of Statistics, The Chinese University of Hong Kong, Shatin NT, Hong Kong, Hong Kong
Venue:
Journal of Multivariate Analysis
Year:
2003

Citing 2
Cited 1

Error rates for classifying observations based on binary and continuous variables with covariates

Computational Statistics & Data Analysis
Non-parametric smoothing of the location model in mixed variable discrimination

Statistics and Computing

Regularized classification for mixed continuous and categorical variables under across-location heteroscedasticity

Journal of Multivariate Analysis

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Abstract

Classification of mixed categorical and continuous data is often performed using the location linear discriminant function which assumes across-location homoscedasticity. In this paper, we investigate the hazard arising from a routine application of the classifier under across-location heteroscedasticity. A limiting and a first-order asymptotic performance index are proposed and studied in a general setting. The first index studies the limiting behavior. The second index corrects the bias due to the finite sample size. Both indexes are illustrated under the assumption of unequal spherical covariance matrices across all the locations. This is likely to be the case in most classification problems dealing with mixed categorical and continuous data. Results of a numerical study are reported.