On least squares estimation with a particular linear function of the dependent variable |
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Institution: | 1. Advanced Control Systems (SAC) Research Group at Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Automatic Control Department, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), C/. Llorens i Artigas 4-6, 08028 Barcelona, Spain;2. Cetaqua, Water Technology Centre, Ctra. d’Esplugues 75, Cornellà de Llobregat, 08940 Barcelona, Spain;1. College of Mathematics and Statistics, Chongqing University, China;2. Department of Mathematics, University of Macau, Macau;3. UMacau Zhuhai Research Institute, China;1. School of Community Health Sciences, University of Nevada, Reno, NV, USA;2. Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA;3. Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA |
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Abstract: | We consider the standard linear regression model where the endogenous variable y is substituted by Ty, T being a symmetric, idempotent matrix. Comparing the mean square error (MSE) matrices we show that a ‘naive’ LS-procedure may work better than a competing estimator usually proposed in the literature and may even perform better than the LS-estimator based on untransformed data. We derive necessary and sufficient conditions for MSE-dominance and outline some ideas for testing. |
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