Estimation in the nonlinear errors-in-variables model |
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| Affiliation: | 1. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA;2. TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA;3. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA;4. State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, China;5. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 43210, USA;6. Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA;1. Research Center for Statistics and Geneva School of Economics and Management, Université de Genève, Bd du Pont d’Arve 40 CH-1211 Geneva 4, Switzerland;2. Operations Department, Faculty of Business and Economics, Université de Lausanne, Bâtiment Anthropole, CH-1015 Lausanne, Switzerland;1. Physical Education Department, Changchun University, Changchun city, Jilin Province, China;2. Department of Statistics, Quaid-e-Azam University, Islamabad, Pakistan;3. Department of Economics, Institute of Development Economics, Islamabad, Pakistan;4. Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt;5. Basic Science Department, Higher Institute of Engineering and Technology, El-Mahala El-Kobra, Egypt;6. Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia |
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| Abstract: | In the nonlinear structural errors-in-variables model we propose a consistent estimator of the unknown parameter, using a modified least squares criterion. Its rate of convergence strongly related to the regularity of the regression function, is generally slower than the parametric rate of convergence n−1/2. Nevertheless, it is of order (log n)r/√n, r > 0, for some analytic regression functions. |
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