Improving ABC for quantile distributions

McVinish, R. (2012) Improving ABC for quantile distributions. Statistics and Computing, 22 6: 1199-1207. doi:10.1007/s11222-010-9209-9

Author McVinish, R.
Title Improving ABC for quantile distributions
Journal name Statistics and Computing   Check publisher's open access policy
ISSN 0960-3174
Publication date 2012-11
Sub-type Article (original research)
DOI 10.1007/s11222-010-9209-9
Volume 22
Issue 6
Start page 1199
End page 1207
Total pages 9
Editor Gilles Celeux
Place of publication Secaucus, NJ, United States
Publisher Springer
Collection year 2013
Language eng
Abstract A new approximate Bayesian computation (ABC) algorithm is proposed specifically designed for models involving quantile distributions. The proposed algorithm compares favourably with two other ABC algorithms when applied to examples involving quantile distributions.
Keyword Approximate Bayesian computation
Likelihood-free inference
Markov chain Monte Carlo
Quantile distributions
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Special Issue on Approximate Bayesian Computation

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2013 Collection
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
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Created: Thu, 22 Nov 2012, 15:51:24 EST by Dr Ross Mcvinish on behalf of Mathematics