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Discriminant Analysis When a Block of Observations is Missing

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Abstract

We consider the problem of classifying a p× 1 observation into one of two multivariate normal populations when the training samples contain a block of missing observations. A new classification procedure is proposed which is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The new discriminant function is easy to use. We consider the estimation of error rate of the linear combination classification procedure by using the leave-one-out estimation and bootstrap estimation. A Monte Carlo study is conducted to evaluate the error rate and the estimation of it. A numerical example is given tof illustrate its use.

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Authors and Affiliations

  1. Department of Industrial and Information Engineering, Kwangju University, Kwangju, 503-703, South Korea

    Hie-Choon Chung

  2. Department of Mathematics, University of Texas at Arlington, Arlington, Tx76019, U.S.A.

    Chien-Pai Han

Authors
  1. Hie-Choon Chung
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  2. Chien-Pai Han
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Chung, HC., Han, CP. Discriminant Analysis When a Block of Observations is Missing. Annals of the Institute of Statistical Mathematics 52, 544–556 (2000). https://doi.org/10.1023/A:1004129706000

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  • DOI: https://doi.org/10.1023/A:1004129706000

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