Bayesian Analysis

Flexible paleoclimate age-depth models using an autoregressive gamma process

Maarten Blaauw and J. Andrés Christen

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Abstract

Radiocarbon dating is routinely used in paleoecology to build chronologies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called "Bacon") that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.

Article information

Source
Bayesian Anal., Volume 6, Number 3 (2011), 457-474.

Dates
First available in Project Euclid: 13 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1339616472

Digital Object Identifier
doi:10.1214/11-BA618

Mathematical Reviews number (MathSciNet)
MR2843540

Zentralblatt MATH identifier
1330.62413

Subjects
Primary: 60K40: Other physical applications of random processes
Secondary: 62F15: Bayesian inference 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62P12: Applications to environmental and related topics 86A60: Geological problems

Keywords
AR Gamma process Radiocarbon Paleoecology Age-Depth models

Citation

Blaauw, Maarten; Christen, J. Andrés. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal. 6 (2011), no. 3, 457--474. doi:10.1214/11-BA618. https://projecteuclid.org/euclid.ba/1339616472


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