Sex Identification in Some Putative Infanticide Victims from Roman Britain Using Ancient DNA

Mays, Simon; Faerman, Marina

doi:10.1006/jasc.2001.0616

Cited by 58 publications

(36 citation statements)

References 15 publications

(26 reference statements)

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“…Miao and Gastwirth, 2004;Tobi et al, 2005). Figure 4 shows plots of coverage probabilities against h for the asymptotic, Agresti-Coull, and exact method 95% confidence intervals using a sample size of 13 following our example by Mays and Faerman (2001). The asymptotic method gives confidence intervals that are too narrow, having coverage probabilities at or slightly above 0.95 only in a tight band around h values of 0.45 and 0.55 and at points 0.21 and 0.79 (coverage plots are symmetric around the center point of h50:5).…”

Section: Of Confidence and Hpdsmentioning

confidence: 99%

“…Mays and Faerman's (2001) study is not the only one to have used aDNA to assess the proportion of males among infants from Roman era sites. After examining their data, Mays and Faerman also included a count of three male infants and one female infant from the Beddingham Roman Villa (Waldron et al, 1999).…”

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

“…Starting with Jeffreys' prior and the Faerman et al (1998) data gives a posterior density of Be 14:5; 5:5 ð Þ which we could use as a prior for Waldron et al's (1999) data to obtain a posterior of Be 17:5; 6:5 ð Þ . This in turn could be used as a prior for Mays and Faerman's (2001) data to arrive at the posterior of Be 26:5; 10:5 ð Þ . Alternatively, we could take the data in reverse order, again beginning with Jeffreys' prior but adding Mays and Faerman's (2001) data to obtain a posterior of Be 9:5; 4:5 ð Þ , then adding Waldron et al's data (1999) data to arrive at a posterior of Be 12:5; 5:5 ð Þ , and finally adding Faerman et al's (1998) data to again arrive at a posterior density of Be 26:5; 10:5 ð Þ .…”

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

“…In the above paragraph, we approached the analysis as if we started with Jeffreys' prior and then used the data on 36 infants (Faerman et al, 1998;Waldron et al, 1999;Mays and Faerman, 2001) to find the posterior density of Be 26:5; 10:5 ð Þ . But we can also use yesterday's posterior as tomorrow's prior, so we could treat the data in the order that it arrived in the literature.…”

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%

“…Throughout this article we use a simple bioanthropological example based on Mays and Faerman's (2001) data on sex identification for 13 infants from two Romano-British cemeteries. The sample size is small because sex identifications were made using ancient DNA (aDNA), but ultimately Mays and Faerman wanted to estimate the proportion of males among all infants from these two sites.…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

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Bayes in biological anthropology

Konigsberg

Frankenberg

2013

American J Phys Anthropol

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In this article, we both contend and illustrate that biological anthropologists, particularly in the Americas, often think like Bayesians but act like frequentists when it comes to analyzing a wide variety of data. In other words, while our research goals and perspectives are rooted in probabilistic thinking and rest on prior knowledge, we often proceed to use statistical hypothesis tests and confidence interval methods unrelated (or tenuously related) to the research questions of interest. We advocate for applying Bayesian analyses to a number of different bioanthropological questions, especially since many of the programming and computational challenges to doing so have been overcome in the past two decades. To facilitate such applications, this article explains Bayesian principles and concepts, and provides concrete examples of Bayesian computer simulations and statistics that address questions relevant to biological anthropology, focusing particularly on bioarchaeology and forensic anthropology. It also simultaneously reviews the use of Bayesian methods and inference within the discipline to date. This article is intended to act as primer to Bayesian methods and inference in biological anthropology, explaining the relationships of various methods to likelihoods or probabilities and to classical statistical models. Our contention is not that traditional frequentist statistics should be rejected outright, but that there are many situations where biological anthropology is better served by taking a Bayesian approach. To this end it is hoped that the examples provided in this article will assist researchers in choosing from among the broad array of statistical methods currently available. Am J Phys Anthropol 57: 2013. V C 2013 Wiley Periodicals, Inc."The Bayesian approach can have a clarifying effect on one's thinking about evidence." (Koehler and Saks, 1991, p 364) Traditional training in statistical methods for those who go on to become practicing biological anthropologists has focused primarily on classical hypothesis testing. This is apparent in both textbooks geared toward anthropologists in general (Thomas, 1986;Madrigal, 1998;Bernard, 2011) and specialized texts for biological anthropologists (Slice, 2005;D'Aoãut and Vereecke, 2011). While Bayes' Theorem may be mentioned in passing in introductory statistics courses, this is typically restricted to examples of such limited interest that the student has little motivation to recall the theorem, and even less motivation to assume that there may be future value in having learned about Bayes' Theorem. In Bayesian terms, the prior probability that the student will retain Bayes' Theorem is quite low. In contrast, the student and eventual practitioner is likely to learn about confidence intervals, Type I and Type II errors in hypothesis testing, and P-values, and to blithely assume that what they have learned represents the near totality of what is available and useful within modern statistical practice. This represents an unfortunate omission of Ba...

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Section: Of Confidence and Hpdsmentioning

confidence: 99%

Section: Sequential Use Of Bayes Theoremmentioning

confidence: 99%