BETA KERNEL SMOOTHERS FOR REGRESSION CURVES

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Nonparametric Filtering of the Realised Spot Volatility: A Kernel-Based Approach (2007, unknown): “By following the arguments in Chen (2000), one can show that E ̂(2) = 2 + L (0)h + o (h ) uniformly over 2 [0; T ], while the variance has the standard form.”
Bivariate frequency analysis of floods using a diffusion based kernel density estimator (2013, methodology): “…method of transformations [e.g., Devroye and Gy€orfi, 1985; Marron and Ruppert, 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,…”
Bernstein polynomial probability density estimation (2004, unknown): “Recently, Chen (1999, 2000) has proposed using some beta density functions as kernels to replace the fixed and symmetric kernel used in the standard kernel method and then introduced variable (boundary) beta kernel estimators, where ‘variable’ means that the shape of beta kernel (more precisely,…”
UNIFORM CONVERGENCE RATES OF KERNEL ESTIMATORS WITH HETEROGENEOUS DEPENDENT DATA (2009, unknown): “Alternatively, one could instead use a local linear estimator or a boundary kernel because these will not suffer from boundary biases (cf., respectively, Cai, 2007; and Chen, 2000), but we here maintain the preceding estimator for simplicity.”