Kernel estimation of regression functions

By Theo Gasser, H. Muller. 1979

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Citations in the corpus

BETA KERNEL SMOOTHERS FOR REGRESSION CURVES (2000, methodology): “To confirm the theoretical expansions for the mean integrated squared errors, theoretical optimal mean integrated squared errors derived from (4.1) and (4.2), (5.4) and from Fan (1993) are also given for the three smoothers.”
Consistent Nonparametric Regression (1977, methodology): “Fritz (1975) studied the NN rule for Model 3.”
Including Covariates in the Regression Discontinuity Design (2018, background): “(See, for example, Jones (1993) or Jones and Foster (1996) for similar boundary kernels, or Gasser and Müller (1979), Gasser, Müller, and Mammitzsch (1985), Müller (1991) or Tenreiro (2013) for a more general discussion on various forms of boundary kernels or boundary corrections including the…”
Analysis of longitudinal data with irregular, outcome‐dependent follow‐up (2004, methodology): “Tail modifications were employed (Gasser and Müller, 1979).”
On automatic boundary corrections (1997, unknown): “It can be shown that, as n!1; h! 0 and nh2 +1 !1, sup f2Cp+1 E " d f ( ) h (0) f ( )(0) 2 jX1; :::;Xn# !C (p + 1)!!2 Z 1 0 tp+1Kp+1; (t) dt 2 h2(p+1 )+ !2 2 (0) nh2 +1gX (0) Z 1 0 Kp+1; (t)2 dt: (19) Minimizing (19) with respect to h; we obtain sup f2Cp+1 E " d f ( ) h (0) f ( )(0) 2 jX1; :::;Xn# p+”
New evidence on returns to scale and product mix among U.S. commercial banks (2001, background): “Gasser and Muller (1979, 1984), Muller (1988), and others have advocated use of high-order kernels for density estimation, typically in the form of even-order (and hence symmetric) polynomials over an interval such as [—1, 1].”
Bivariate frequency analysis of floods using a diffusion based kernel density estimator (2013, methodology): “…1994], and use of nonconventional kernels such as beta kernels [e.g., Chen, 1999, 2000; Bouezmarni and Scaillet, 2003], gamma kernels [e.g., Bouezmarni and Rombouts, 2009], boundary kernels [e.g., Gasser and M€uller, 1979], and adaptive kernels [e.g., Terrell and Scott, 1992; Botev et al., 2010].”
Locally Weighted Learning (1997, methodology): “He uses the k closest points to form the local model. The distance to the nearest point is used as an estimate of the con dence in the prediction. A clustering process on the inputs and the outputs (xi; yi) is used to handle noise and one to many mapping problems. A k-d tree is used to speed up near”
Bernstein polynomial probability density estimation (2004, unknown): “See, for example, Gasser and Müller (1979), Gasser et al. (1985), and Müller (1988, 1991).”