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On discrimination and classification with multivariate repeated measures data
Institution:1. Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonio, TX 78249,USA;2. Department of Mathematics and Statistics, Oakland University, Rochester, MI 48309, USA;1. Risk Analysis Research Center, The Institute of Statistical Mathematics, 10–3 Midori-cho, Tachikawa-shi, Tokyo 190–8562, Japan;2. Faculty of Economics, University of Tokyo, 7–3–1 Hongo, Bunkyo-ku, Tokyo 113–0033, Japan;1. Department of Mathematical Sciences, FO 35, University of Texas at Dallas, Richardson, TX 75083-0688, USA;2. School of Earth & Environmental Sciences, G41a Mawson Laboratories, University of Adelaide, North Terrace, SA 5005, Australia;1. Department of Liberal Arts, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki Noda, 278–8510 Chiba, Japan;2. Institut für Stochastik, Fakultät für Mathematik, Karlsruher Institut für Technologie, Kaiserstraße 89, 76133 Karlsruhe, Germany;1. Experimental Nuclear Physics Department, Nuclear Research Centre, P.O.13759, Cairo, Egypt;2. Institute of High Energy Physics, CAS, Beijing 100049, China
Abstract:We study the problem of classification with multiple q-variate observations with and without time effect on each individual. We develop new classification rules for populations with certain structured and unstructured mean vectors and under certain covariance structures. The new classification rules are effective when the number of observations is not large enough to estimate the variance–covariance matrix. Computational schemes for maximum likelihood estimates of required population parameters are given. We apply our findings to two real data sets as well as to a simulated data set.
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