Bootstrap-Based Bandwidth Selection for Semiparametric Generalized Regression Estimators

Optimal bandwidth selection for semi-recursive kernel regression estimators

In this paper we propose an automatic selection of the bandwidth of the semi-recursive kernel estimators of a regression function defined by the stochastic approximation algorithm. We showed that, using the selected bandwidth and some special stepsizes, the proposed semi-recursive estimators will be very competitive to the nonrecursive one in terms of estimation error but much better in terms o...

Bootstrap Bandwidth Selection

Various bootstrap methodologies are discussed for the selection of the bandwidth of a kernel density estimator. The smoothed bootstrap is seen to provide new and independent motivation of some previously proposed methods. A curious feature of bootstrapping in this context is that no simulated resampling is required, since the needed functionals of the distribution can be calculated explicitly.

Generalized Ridge Regression Estimator in Semiparametric Regression Models

In the context of ridge regression, the estimation of ridge (shrinkage) parameter plays an important role in analyzing data. Many efforts have been put to develop skills and methods of computing shrinkage estimators for different full-parametric ridge regression approaches, using eigenvalues. However, the estimation of shrinkage parameter is neglected for semiparametric regression models. The m...

SEMIPARAMETRIC ESTIMATORS IN COMPETING RISKS REGRESSION by

New Bandwidth Selection for Kernel Quantile Estimators

We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal bandwidth. This method is shown to lead to asymptotically...