Estimation of two order restricted normal means with unknown and possibly unequal variances |
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| Institution: | 1. School of Science for OPEN and Environmental Systems, Graduate School of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku, Yokohama 223-8522, Japan;2. Department of Administration Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku, Yokohama 223-8522, Japan;1. Institute of Statistics, RWTH Aachen University, Wüllnerstr. 3, D-52056 Aachen, Germany;2. Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA), Université catholique de Louvain, Voie du Roman Pays 20, B-1348 Louvain-la-Neuve, Belgium;1. College of Economics and Management, China Jiliang University, Hangzhou, China;2. Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China |
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| Abstract: | Estimation of each of and linear functions of two order restricted normal means is considered when variances are unknown and possibly unequal. We replace unknown variances with sample variances and construct isotonic regression estimators, which we call in our paper the plug-in estimators, to estimate ordered normal means. Under squared error loss, a necessary and sufficient condition is given for the plug-in estimators to improve upon the unrestricted maximum likelihood estimators uniformly. As for the estimation of linear functions of ordered normal means, we also show that when variances are known, the restricted maximum likelihood estimator always improves upon the unrestricted maximum likelihood estimator uniformly, but when variances are unknown, the plug-in estimator does not always improve upon the unrestricted maximum likelihood estimator uniformly. |
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