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• W2022348805 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." @default.
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• W2022348805 date "2010-07-01" @default.
• W2022348805 modified "2023-10-16" @default.
• W2022348805 title "Density estimation using asymmetric kernels and Bayes bandwidths with censored data" @default.
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• W2022348805 doi "https://doi.org/10.1016/j.jspi.2010.01.001" @default.
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