A Bayesian model for local smoothing in kernel density estimation
Statistics and Computing
Multivariate locally adaptive density estimation
Computational Statistics & Data Analysis
Comparison of presmoothing methods in kernel density estimation under censoring
Computational Statistics
A Bayesian approach to bandwidth selection for multivariate kernel density estimation
Computational Statistics & Data Analysis
Fast kernel conditional density estimation: A dual-tree Monte Carlo approach
Computational Statistics & Data Analysis
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Image and Vision Computing
Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation
Computational Statistics & Data Analysis
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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. Monte Carlo simulation results show that the tail-adaptive density estimator outperforms the global-bandwidth density estimators implemented using different global bandwidth selection rules. The inferential potential of the tail-adaptive density estimator is demonstrated by employing the estimator to estimate the bivariate density of daily index returns observed from the USA and Australian stock markets.