Boundary and Bias Correction in Kernel Hazard Estimation
β Scribed by Jens Perch Nielsen; Carsten Tanggaard
- Book ID
- 108536166
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 323 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0303-6898
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
The kernel method of estimation of curves is now popular and widely used in statistical applications. Kernel estimators suffer from boundary effects, however, when the support of the function to be estimated has finite endpoints. Several solutions to this problem have already been proposed. Here the
We propose a new estimator for boundary correction for kernel density estimation. Our method is based on local Bayes techniques of Hjort (Bayesian Statist. 5 (1996) 223). The resulting estimator is semiparametric type estimator: a weighted average of an initial guess and the ordinary re ection metho
for ordinary nonparametric kernel regression and for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative estimation and multiple bandwidths of different order. We deri