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• W3011297583 abstract "Estimation of density derivatives has found multiple uses in statistical data analysis. An inefficient two-step method to obtain it is estimating the density and then computing the derivatives. This method does not render good results since a good density estimator is not always a suitable density-derivative estimator. The present paper studies the kernel type as a non-parametric estimation of the density function derivative connected with a highly mixing time series. To improve estimation accuracy in asymptotic mean squared error sense, a shrinkage type estimator is defined, under the prior information that the derivative is known. In addition to the investigation of asymptotic distributional properties, a simulation study is carried out to numerically demonstrate the findings. Effect of deviating from the prior information on estimation is also considered and discussed." @default.
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• W3011297583 date "2020-08-01" @default.
• W3011297583 modified "2023-10-16" @default.
• W3011297583 title "Density derivative estimation for stationary and strongly mixing data" @default.
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• W3011297583 doi "https://doi.org/10.1016/j.aej.2020.02.024" @default.
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