Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions

Shuowen Hu, Donald Poskitt and Xibin Zhang ()

Computational Statistics & Data Analysis, 2012, vol. 56, issue 3, 732-740

Abstract: 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.

Keywords: Marginal likelihood; Markov chain Monte Carlo; S&P 500 index; Value-at-risk (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Bayesian Adaptive Bandwidth Kernel Density Estimation of Irregular Multivariate Distributions (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:3:p:732-740

DOI: 10.1016/j.csda.2011.09.022

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