Bias correction for outlier estimation in time series |
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| Institution: | 1. Department of Chemistry, Maris Stella College, Vijayawada, Andhra Pradesh, India;2. Department of Physics, Maris Stella College, Vijayawada, Andhra Pradesh, India;3. Department of Chemistry, Andhra Loyola College, Vijayawada, Andhra Pradesh, India;4. Department of Chemistry, NRI Institute of Technology, Pothavarappadu, Andhra Pradesh, India;5. Department of Chemistry, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India;1. Department of Chemistry, Veer Narmad South Gujarat University, Surat 395007, India;2. Shah-Schulman Center for Surface Science & Nanotechnology, Dharmsinh Desai University, Nadiad 387001, India;1. School of Resources and Environment, Anhui Agricultural University, 230026 Hefei, PR China;2. Anhui Environmental Monitoring Center, 230061 Hefei, PR China;3. School of Chemical Engineering, Qinghai University, 810016 Xining, China;4. Institute of Plasma Physics, Chinese Academy of Science, P.O. Box 1126, Hefei 230031, PR China;1. School of Chemical Engineering, Shandong University of Technology, 255049 Zibo, Shandong, PR China;2. New Star Institute of Applied Technology, No. 451 Huangshan Road, Hefei, Anhui 230031, PR China |
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| Abstract: | The problem of outlier estimation in time series is addressed. The least squares estimators of additive and innovation outliers in the framework of linear stationary and non-stationary models are considered and their bias is evaluated. As a result, simple alternative nearly unbiased estimators are proposed both for the additive and the innovation outlier types. A simulation study confirms the theoretical results and suggests that the proposed estimators are effective in reducing the bias also for short series. |
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