Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

Xibin Zhang (), Maxwell King and Rob Hyndman

No 9/04, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We provide Markov chain Monte Carlo (MCMC) algorithms for computing the bandwidth matrix for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters to be estimated, which we do by optimizing the likelihood cross-validation criterion. Numerical results show that the resulting bandwidths are superior to all existing methods; for dimensions greater than two, our algorithm is the first practical method for estimating the optimal bandwidth matrix. Moreover, the MCMC algorithm for bandwidth selection for multivariate data has no increased difficulty as the dimension of data increases.

Keywords: Bandwidth selection; cross-validation; multivariate kernel density estimation; sampling algorithms. (search for similar items in EconPapers)
JEL-codes: C11 C14 C51 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2004-04
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (4)

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Working Paper: Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC (2004)
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