Nonparametric regression estimates with censored data based on block thresholding method |
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| Authors: | E Shirazi H Doosti HA Niroumand N Hosseinioun |
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| Institution: | 1. Department of Statistics, Faculty of Science, Gonbad Kavous University, Gonbad Kavous, Iran;2. Department of Mathematics, Kharazmi University, Tehran, Iran;3. Department of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia;4. Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran;5. Department of statistics, Payame Noor University, 19395-4697 Tehran, Iran |
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| Abstract: | Here we consider wavelet-based identification and estimation of a censored nonparametric regression model via block thresholding methods and investigate their asymptotic convergence rates. We show that these estimators, based on block thresholding of empirical wavelet coefficients, achieve optimal convergence rates over a large range of Besov function classes, and in particular enjoy those rates without the extraneous logarithmic penalties that are usually suffered by term-by-term thresholding methods. This work is extension of results in Li et al. (2008). The performance of proposed estimator is investigated by a numerical study. |
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| Keywords: | Block thresholding Censored data Minimax estimation Nonparametric regression Rate of convergence |
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