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- W2000911035 abstract "In medical diagnostics data are often characterized by repeated observations on the same subject (patient), particularly in form of time series, dose-response curves etc. If the aim is an individual allocation of patients into one of different groups (diagnoses), a discriminant analysis has to be applied. In a first attempt, Tomasko et al., 1999 [5] modified the well-known linear discriminant analysis using the mixed model MANOVA for the estimation of fixed effects and for a determination of various structures of covariance matrices, including unstructured, compound symmetry, and autoregressive of order 1. We extended that approach by a random subject effect (Azzalani, 1995 [1]), validated error estimations, and cross-validated feature selection (Wernecke, 2002 [9]). In this paper, we complete the investigations of the new method with a cross-validated selection of the adequate model (covariance structure) and extensive Monte-Carlo simulations. An application to contrast-enhanced breast MRI signals in a multicenter study is also given. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)" @default.
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- W2000911035 date "2004-04-01" @default.
- W2000911035 modified "2023-10-18" @default.
- W2000911035 title "A Mixed Model Approach to Discriminant Analysis with Longitudinal Data" @default.
- W2000911035 doi "https://doi.org/10.1002/bimj.200310020" @default.
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