Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 29 September 2011 Shuowen Hu, D.S. Poskitt, Xibin Zhang In this paper, we propose a new methodology for multivariate kernel density estimation in which data are categorized into low- and high-density regions as an underlying mechanism for assigning adaptive bandwidths. We derive the posterior density of the bandwidth parameters via the Kullback–Leibler divergence criterion and use a Markov chain Monte Carlo (MCMC) sampling algorithm to estimate the adaptive bandwidths. The resulting estimator is referred to as the tail-adaptive density estimator

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 29 September 2011 Shuowen Hu, D.S. Poskitt, Xibin Zhang In this paper, we propose a new methodology for multivariate kernel density estimation in which data are categorized into low- and high-density regions as an underlying mechanism for assigning adaptive bandwidths. We derive the posterior density of the bandwidth parameters via the Kullback–Leibler divergence criterion and use a Markov chain Monte Carlo (MCMC) sampling algorithm to estimate the adaptive bandwidths. The resulting estimator is referred to as the tail-adaptive density estimator

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Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions

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