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

Chi-Ying Leung

Journal of Multivariate Analysis, 2005, vol. 93, issue 2, 358-374

Abstract: A regularized classifier is proposed for a two-population classification problem of mixed continuous and categorical variables in a general location model(GLOM). The limiting overall expected error for the classifier is given. It can be used in an optimization search for the regularization parameters. For a heteroscedastic spherical dispersion across all locations, an asymptotic error is available which provides an alternative criterion for the optimization search. In addition, the asymptotic error can serve as a baseline for practical comparisons with other classifiers. Results based on a simulation and two real datasets are presented.

Keywords: Regularized; discrimination; Location; linear; discriminant; function; Spherically; symmetric; across-location; dispersion; Limiting; expected; overall; error; Asymptotic; expansion (search for similar items in EconPapers)
Date: 2005
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