Hostname: page-component-745bb68f8f-mzp66 Total loading time: 0 Render date: 2025-01-12T22:59:35.907Z Has data issue: false hasContentIssue false
  • English
  • Français

Dealing with Outliers and Offsets in Radiocarbon Dating

Published online by Cambridge University Press:  18 July 2016

Christopher Bronk Ramsey
Christopher Bronk Ramsey*
Affiliation:
Research Laboratory for Archaeology, University of Oxford, Dyson Perrins Building, South Parks Road, Oxford, OX1 3QY, United Kingdom. Email: christopher.ramsey@rlaha.ox.ac.uk
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The wide availability of precise radiocarbon dates has allowed researchers in a number of disciplines to address chronological questions at a resolution which was not possible 10 or 20 years ago. The use of Bayesian statistics for the analysis of groups of dates is becoming a common way to integrate all of the 14C evidence together. However, the models most often used make a number of assumptions that may not always be appropriate. In particular, there is an assumption that all of the 14C measurements are correct in their context and that the original 14C concentration of the sample is properly represented by the calibration curve.

In practice, in any analysis of dates some are usually rejected as obvious outliers. However, there are Bayesian statistical methods which can be used to perform this rejection in a more objective way (Christen 1994b), but these are not often used. This paper discusses the underlying statistics and application of these methods, and extensions of them, as they are implemented in OxCal v 4.1. New methods are presented for the treatment of outliers, where the problems lie principally with the context rather than the 14C measurement. There is also a full treatment of outlier analysis for samples that are all of the same age, which takes account of the uncertainty in the calibration curve. All of these Bayesian approaches can be used either for outlier detection and rejection or in a model averaging approach where dates most likely to be outliers are downweighted.

Another important subject is the consistent treatment of correlated uncertainties between a set of measurements and the calibration curve. This has already been discussed by Jones and Nicholls (2001) in the case of marine reservoir offsets. In this paper, the use of a similar approach for other kinds of correlated offset (such as overall measurement bias or regional offsets in the calibration curve) is discussed and the implementation of these methods in OxCal v 4.0 is presented.

Type
Statistical Applications
Information
Radiocarbon , Volume 51 , Issue 3 , 2009 , pp. 1023 - 1045
Copyright
Copyright © 2009 by the Arizona Board of Regents on behalf of the University of Arizona 

References

REFERENCES

Abraham, B, Box, GEP. 1978. Linear models and spurious observations. Applied Statistics 27(2):131–8.CrossRefGoogle Scholar
Blaauw, M, Heuvelink, GBM, Mauquoy, D, van der Plicht, J, van Geel, B. 2003. A numerical approach to 14C wiggle-match dating of organic deposits: best fits and confidence intervals. Quaternary Science Reviews 22(14):1485–500.CrossRefGoogle Scholar
Blockley, S, Blaauw, M, Bronk Ramsey, C, van der Plicht, J. 2007. Building and testing age models for radiocarbon dates in Lateglacial and Early Holocene sediments. Quaternary Science Reviews 26(15–16):1915–26.CrossRefGoogle Scholar
Boaretto, E, Jull, AJT, Gilboa, A, Sharon, I. 2005. Dating the Iron Age I/II transition in Israel: first intercomparison results. Radiocarbon 47(1):39–55.CrossRefGoogle Scholar
Bronk Ramsey, C. 1995. Radiocarbon calibration and analysis of stratigraphy: the OxCal program. Radiocarbon 37(2):425–30.CrossRefGoogle Scholar
Bronk Ramsey, C. 2001. Development of the radiocarbon calibration program. Radiocarbon 43(2A):355–63.Google Scholar
Bronk Ramsey, C. 2008. Deposition models for chronological records. Quaternary Science Reviews 27(1–2):42–60.CrossRefGoogle Scholar
Bronk Ramsey, C, van der Plicht, J, Weninger, B. 2001. ‘Wiggle matching’ radiocarbon dates. Radiocarbon 43(2A):381–9.Google Scholar
Buck, CE, Christen, JA, James, GN. 1999. BCal: an on-line Bayesian radiocarbon calibration tool. Internet Archaeology 7. Available at http://intarch.ac.uk/journal/issue7/buck_index.html.Google Scholar
Christen, JA. 1994a. Bayesian interpretation of radiocarbon results [PhD dissertation]. University of Nottingham.Google Scholar
Christen, JA. 1994b. Summarizing a set of radiocarbon determinations: a robust approach. Applied Statistics-Journal of the Royal Statistical Society Series C 43(3):489–503.Google Scholar
Christen, JA. 2003. Bwigg: an internet facility for Bayesian radiocarbon wiggle matching. Internet Archaeology 7. Available at http:/intarch.ac.uk/journal/issue13/christe_index.html.Google Scholar
Imamura, M, Ozaki, H, Mitsutani, T, Niu, E, Itoh, S. 2007. Radiocarbon wiggle-matching of Japanese historical materials with a possible systematic age offset. Radiocarbon 49(2):331–7.Google Scholar
Jones, M, Nicholls, G. 2001. Reservoir offset models for radiocarbon calibration. Radiocarbon 43(1):119–24.CrossRefGoogle Scholar
Nicholls, G, Jones, M. 2001. Radiocarbon dating with temporal order constraints. Applied Statistics- Journal of the Royal Statistical Society Series C 50(4):503–21.Google Scholar
Sharon, I, Gilboa, A, Jull, AJT, Boaretto, E. 2007. Report on the first stage of the Iron Age Dating Project in Israel: supporting a Low Chronology. Radiocarbon 49(1):1–46.Google Scholar
Stuiver, M, Braziunas, TF. 1993. Modeling atmospheric 14C influences and 14C ages of marine samples to 10,000 BC. Radiocarbon 35(1):137–89.CrossRefGoogle Scholar
Venables, WN, Ripley, BD. 2002. Modern Applied Statistics. 4th edition. New York: Springer-Verlag.CrossRefGoogle Scholar
Ward, GK, Wilson, SR. 1978. Procedures for comparing and combining radiocarbon age determinations: a critique. Archaeometry 20(1):19–31.Google Scholar