Function estimation using data-adaptive kernel smoothers—how much smoothing?

@article{Riedel1994FunctionEU,
  title={Function estimation using data-adaptive kernel smoothers—how much smoothing?},
  author={Kurt S. Riedel and Alexander Sidorenko},
  journal={Computers in Physics},
  year={1994},
  volume={8},
  pages={402-409}
}
AbstractThe following sections are included:Bias-Versus-Variance Trade-offLocal Error and Optimal KernelsHow to Select the HalfwidthPlug-in-Derivative Estimates of the Local HalfwidthData-adaptive SmoothingFurther ReadingAcknowledgmentsReferences 

Adaptive smoothing of the log-spectrum with multiple tapering

  • K. Riedel
  • Computer Science
    IEEE Trans. Signal Process.
  • 1996
A hybrid estimator of the log-spectral density of a stationary time series is proposed, which reduces the expected mean square error by (/spl pi//sup 2//4)/sup 4/5/ over simply smoothing the log tapered periodogram.

Piecewise convex estimation for signal processing

  • K. Riedel
  • Mathematics
    1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings
  • 1996
This work outline how piecewise convex fitting may be applied to signal recovery, instantaneous frequency estimation, surface reconstruction, image segmentation, spectral estimation and multivariate adaptive regression, and two distinct methodologies for shape-correct estimation are given.

Smoothed multiple taper spectral analysis and adaptive implementation

A hybrid estimator of the log-spectral density of a stationary time series is proposed, which reduces the expected mean square error by ((/spl pi//sup 2/)/4)/sup 0.8/ over simply smoothing the log tapered periodogram.

Riedel Signal Processing and Piecewise Convex Estimation

Many problems on signal processing reduce to nonparametric function estimation. We propose a new methodology, piecewise convex fitting (PCF), and give a two-stage adaptive estimate. In the first

Signal Processing and Piecewise Convex Estimation

Many problems on signal processing reduce to nonparametric function estimation. We propose a new methodology, piecewise convex fitting (PCF), and give a two-stage adaptive estimate. In the first

Nonparametric smoothing of interferometric height maps using confidence values

We use an extension of normalized convolution to smooth height maps from interferometry using confidence values. The latter are often used for dichotomous good/bad decisions only, with all bad data

Kernel estimation of the instantaneous frequency

  • K. Riedel
  • Physics, Computer Science
    IEEE Trans. Signal Process.
  • 1994
The author shows that estimating the instantaneous frequency corresponds to estimating the first derivative of a modulated signal, A(t)exp(i/spl phi/(t), and the halfwidth which minimizes the expected error is larger.

Minimum bias multiple taper spectral estimation

Two families of orthonormal tapers are proposed for multitaper spectral analysis: minimum bias tapers, and sinusoidal tapers {/spl upsisup (k/)}, where /spl upsisub nsup (k/)=/spl

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