Density estimation using asymmetric kernels and Bayes bandwidths with censored data |
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Authors: | CN Kuruwita KB Kulasekera WJ Padgett |
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Institution: | 1. Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, USA;2. Department of Statistics, University of South Carolina, Columbia, SC 29208, USA |
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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. |
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Keywords: | Kernel density estimation Pivoting Bayesian bandwidths Censored data |
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