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Author

R.F. Galbraith

Other affiliations: Aberystwyth University
Bio: R.F. Galbraith is an academic researcher from University College London. The author has contributed to research in topics: Fission track dating & Population. The author has an hindex of 26, co-authored 41 publications receiving 6932 citations. Previous affiliations of R.F. Galbraith include Aberystwyth University.

Papers
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TL;DR: In this paper, the authors outline the background to the optical dating program at Jinmium, and describe the experimental design and statistical methods used to obtain optical ages from single grains of quartz sand.
Abstract: Jinmium rock shelter is famous for the claims made by Fullagar et al. (1996) for the early human colonization and ancient rock art of northern Australia. These claims were based on thermo-luminescence ages obtained for the artefact-bearing quartz sediments that form the floor deposit at the site. In this paper, we outline the background to the optical dating programme at Jinmium, and describe the experimental design and statistical methods used to obtain optical ages from single grains of quartz sand. The results, interpretations, and implications of this dating programme are reported in a companion paper (Roberts et al. 1999, this volume).

2,042 citations

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TL;DR: In this paper, finite mixtures and two new infinite mixture models were proposed to estimate various features of interest such as the minimum age, the other component ages and the age dispersion.

872 citations

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TL;DR: In this article, the statistical estimation of uncertainties and variation for comparing and interpreting age estimates, with specific reference to the estimation of equivalent dose (De) values in the optically stimulated luminescence (OSL) dating of sediments.

517 citations

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TL;DR: In this article, the problem of estimating the component ages of a sample containing grains of different true ages was considered and formulae for estimating these parameters along with their relative standard errors, confidence intervals and appropriate diagnostics when the grains are dated by the external detector method.

498 citations

Journal ArticleDOI
31 Oct 2008-Science
TL;DR: Age ages for nine sites from varied climatic and ecological zones across southern Africa show that both industries were short-lived (5000 years or less), separated by about 7000 years, and coeval with genetic estimates of population expansion and exit times.
Abstract: The expansion of modern human populations in Africa 80,000 to 60,000 years ago and their initial exodus out of Africa have been tentatively linked to two phases of technological and behavioral innovation within the Middle Stone Age of southern Africa-the Still Bay and Howieson's Poort industries-that are associated with early evidence for symbols and personal ornaments. Establishing the correct sequence of events, however, has been hampered by inadequate chronologies. We report ages for nine sites from varied climatic and ecological zones across southern Africa that show that both industries were short-lived (5000 years or less), separated by about 7000 years, and coeval with genetic estimates of population expansion and exit times. Comparison with climatic records shows that these bursts of innovative behavior cannot be explained by environmental factors alone.

494 citations


Cited by
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TL;DR: It is concluded that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity, and one or both should be presented in publishedMeta-an analyses in preference to the test for heterogeneity.
Abstract: The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.

25,460 citations

Journal ArticleDOI
TL;DR: In this paper, Heaton, AG Hogg, KA Hughen, KF Kaiser, B Kromer, SW Manning, RW Reimer, DA Richards, JR Southon, S Talamo, CSM Turney, J van der Plicht, CE Weyhenmeyer
Abstract: Additional co-authors: TJ Heaton, AG Hogg, KA Hughen, KF Kaiser, B Kromer, SW Manning, RW Reimer, DA Richards, JR Southon, S Talamo, CSM Turney, J van der Plicht, CE Weyhenmeyer

13,605 citations

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TL;DR: The metafor package provides functions for conducting meta-analyses in R and includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models.
Abstract: The metafor package provides functions for conducting meta-analyses in R. The package includes functions for fitting the meta-analytic fixed- and random-effects models and allows for the inclusion of moderators variables (study-level covariates) in these models. Meta-regression analyses with continuous and categorical moderators can be conducted in this way. Functions for the Mantel-Haenszel and Peto's one-step method for meta-analyses of 2 x 2 table data are also available. Finally, the package provides various plot functions (for example, for forest, funnel, and radial plots) and functions for assessing the model fit, for obtaining case diagnostics, and for tests of publication bias.

11,237 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the interpretation of the sensitivity corrected growth curve as a function of dose, and the effect of changing measurement conditions (e.g., preheat temperature, size of test dose, stimulation temperature) on the estimation of De.

4,287 citations

Journal ArticleDOI
TL;DR: An adaption of Egger regression can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations, and provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.
Abstract: Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

3,392 citations