Extension of model-based classification for binary data when training and test populations differ

J. Jacques and C. Biernacki

Journal of Applied Statistics, 2010, vol. 37, issue 5, 749-766

Abstract: Standard discriminant analysis supposes that both the training sample and the test sample are derived from the same population. When these samples arise from populations differing in their descriptive parameters, a generalization of discriminant analysis consists of adapting the classification rule related to the training population to another rule related to the test population, by estimating a link map between both populations. This paper extends an existing work in the multinormal context to the case of binary data. In order to solve the problem of defining a link map between the two binary populations, it is assumed that the binary data result from the discretization of latent Gaussian data. An estimation method and a robustness study are presented, and two applications in a biological context illustrate this work.

Keywords: Biological application; discriminant analysis; EM algorithm; latent class model; Stochastic link (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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DOI: 10.1080/02664760902889957

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