On the maximum likelihood classification rule for incomplete multivariate samples and its admissibility

J. N. Srivastava and M. K. Zaatar

Journal of Multivariate Analysis, 1972, vol. 2, issue 1, 115-126

Abstract: In this paper we deal with the problem of classifying a multiresponse observation into one of two p-variate normal populations with unknown mean vectors and a known and common dispersion matrix. The classification procedure is based on two general incomplete multiresponse samples (i.e., not all responses are measured on each sampling unit), one from each population. We obtain the maximum likelihood classification rule and prove its admissibility with respect to a loss function of which the zero-one loss function is a sqecial case.

Keywords: General; incomplete; multiresponse; (multivariate); models; classification; procedures; maximum; likelihood; methods; admissibility (search for similar items in EconPapers)
Date: 1972
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