A Bayesian model for local smoothing in kernel density estimation
Statistics and Computing
Multivariate locally adaptive density estimation
Computational Statistics & Data Analysis
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Computational Statistics & Data Analysis
Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions
Computational Statistics & Data Analysis
Full bandwidth matrix selectors for gradient kernel density estimate
Computational Statistics & Data Analysis
Bandwidth selection for backfitting estimation of semiparametric additive models: A simulation study
Computational Statistics & Data Analysis
Bandwidth selection in marker dependent kernel hazard estimation
Computational Statistics & Data Analysis
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Bandwidth selection in multivariate kernel density estimation has received considerable attention. In addition to classical methods of bandwidth selection, such as plug-in and cross-validation methods, Bayesian approaches have also been previously investigated. Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation is investigated, when the quadratic and entropy loss functions are used. Under the quadratic loss function, the proposed method is evaluated through a simulation study and two real data sets, which were already discussed in the literature. For these real-data applications, very interesting advantages of the proposed method are pointed out.