4 papers · 1 filter
Optimal Boundary Kernels and Weightings for Local Polynomial Regression
Alexander Sidorenko, Kurt S. Riedel
Kernel smoothers are considered near the boundary of the interval. Kernels which minimize the expected mean square error are derived. These kernels are equivalent to using a linear…
Sufficient Conditions for a Linear Estimator to be a Local Polynomial Regression
Alexander Sidorenko, Kurt S. Riedel
It is shown that any linear estimator that satisfies the moment conditions up to order is equivalent to a local polynomial regression of order with some non-negative weight…
Function Estimation Using Data Adaptive Kernel Estimation - How Much Smoothing?
Kurt S. Riedel, A. Sidorenko
We determine the expected error by smoothing the data locally. Then we optimize the shape of the kernel smoother to minimize the error. Because the optimal estimator depends on the…
Adaptive Kernel Estimation of the Spectral Density with Boundary Kernel Analysis
Alexander Sidorenko, Kurt S. Riedel
A hybrid estimator of the log-spectral density of a stationary time series is proposed. First, a multiple taper estimate is performed, followed by kernel smoothing the log-multitap…