Convolution type estimators for nonparametric regression

@inproceedings{Mack1988ConvolutionTE,
  title={Convolution type estimators for nonparametric regression},
  author={Ybonne Mack and Hans-Georg M{\"u}ller},
  year={1988}
}
Convolution type kernel estimators such as the Priestley-Chao estimator have been discussed by several authors in the fixed design regression model Yi = g(ti)+ [var epsilon]i, where [var epsilon]i are uncorrelated random errors, ti are fixed design points where measurements are made, and g is the function to be estimated from the noisy measurements Yi. Using properties of order statistics and concomitants, we derive the asymptotic mean squared error of these estimators in the random design case… CONTINUE READING

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