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

Listed author(s):
  • Shao, Wei
  • Guo, Guangbao
  • Meng, Fanyu
  • Jia, Shuqin

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.

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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 57 (2013)
Issue (Month): 1 ()
Pages: 465-478

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Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:465-478
DOI: 10.1016/j.csda.2012.07.014
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  1. S. Illeris & G. Akehurst, 2002. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 22(1), pages 1-3, January.
  2. Petris, Giovanni, 2010. "An R Package for Dynamic Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i12).
  3. Liang F. & Wong W.H., 2001. "Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 653-666, June.
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