Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149192 | Journal of Statistical Planning and Inference | 2010 | 10 Pages |
Abstract
We propose a modification to the regular kernel density estimation method that use asymmetric kernels to circumvent the spill over problem for densities with positive support. First a pivoting method is introduced for placement of the data relative to the kernel function. This yields a strongly consistent density estimator that integrates to one for each fixed bandwidth in contrast to most density estimators based on asymmetric kernels proposed in the literature. Then a data-driven Bayesian local bandwidth selection method is presented and lognormal, gamma, Weibull and inverse Gaussian kernels are discussed as useful special cases. Simulation results and a real-data example illustrate the advantages of the new methodology.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
C.N. Kuruwita, K.B. Kulasekera, W.J. Padgett,