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Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time
Authors:Ricardo Leiva  Anuradha Roy
Institution:1. Departamento de Matemática, F.C.E., Universidad Nacional de Cuyo, 5500 Mendoza, Argentina;2. Department of Management Science and Statistics, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA
Abstract:In this article we study the problem of classification of three-level multivariate data, where multiple qq-variate observations are measured on uu-sites and over pp-time points, under the assumption of multivariate normality. The new classification rules with certain structured and unstructured mean vectors and covariance structures are very efficient in small sample scenario, when the number of observations is not adequate to estimate the unknown variance–covariance matrix. These classification rules successfully model the correlation structure on successive repeated measurements over time. Computation algorithms for maximum likelihood estimates of the unknown population parameters are presented. Simulation results show that the introduction of sites in the classification rules improves their performance over the existing classification rules without the sites.
Keywords:primary  62H30  secondary  62H12
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