The Kernel Estimate of a Regression Function in Likelihood-Based Models

@inproceedings{Staniswalis1989TheKE,
  title={The Kernel Estimate of a Regression Function in Likelihood-Based Models},
  author={Joan G. Staniswalis},
  year={1989}
}
Abstract Smoothing splines have a penalized likelihood motivation (Good and Gaskins 1971) allowing direct application to nonparametric regression in likelihood-based models. The notion of a weighted likelihood for the nonparametric kernel estimation of a regression function is proposed, generalizing the local likelihood theory of Tibshirani and Hastie (1987). Let the data be of the form (xi, Yi ) (i = 1, …, n), where xi ∈ [0, 1] d are lattice points and the Yi are independent random variables… CONTINUE READING

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