An efficient proposal distribution for Metropolis–Hastings using a B-splines technique

Wei Shao, Guangbao Guo (), Fanyu Meng and Shuqin Jia

Computational Statistics & Data Analysis, 2013, vol. 57, issue 1, 465-478

Abstract: In this paper, we proposed an efficient proposal distribution in the Metropolis–Hastings algorithm using the B-spline proposal Metropolis–Hastings algorithm. This new method can be extended to high-dimensional cases, such as the B-spline proposal in Gibbs sampling and in the Hit-and-Run (BSPHR) algorithm. It improves the proposal distribution in the Metropolis–Hastings algorithm by carrying more information from the target function. The performance of BSPHR was compared with that of other Markov Chain Monte Carlo (MCMC) samplers in simulation and real data examples. Simulation results show that the new method performs significantly better than other MCMC methods.

Keywords: Efficient proposal distribution; Metropolis–Hastings algorithm; B-splines; Gibbs sampling; Markov Chain Monte Carlo (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:57:y:2013:i:1:p:465-478

DOI: 10.1016/j.csda.2012.07.014

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