Estimating the Distribution Function of a Stationary Process Involving Measurement Errors |
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Authors: | Ioannides DA Papanastassiou DP |
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Institution: | (1) Department of Economics, University of Macedonia, 54006 Thessaloniki, Greece;(2) Department of Applied Informatics, University of Macedonia, 54006 Thessaloniki, Greece |
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Abstract: | A nonparametric estimation of a distribution function is considered when observations contain measurement errors. A method
is developed to establish asymptotic normality results for a deconvoluting kernel-type estimator for ρ-mixing stochastic processes
corrupted by some noise process. It is shown that the asymptotic distribution depends on the smoothness of the noise distributions,
which are characterized as either ordinary smooth or super smooth. Also, the kind of dependence of the noise process is crucial
to the form of the asymptotic variance.
This revised version was published online in June 2006 with corrections to the Cover Date. |
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Keywords: | deconvolution nonparametric estimation distribution function noise distribution ρ -mixing |
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