A Parametric Bootstrap for the Mean Measure of Divergence

Zertuche F. () and Meza-Peñaloza A. ()
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Zertuche F.: Unidad Cuernavaca Instituto de Matemáticas, Universidad Nacional Autonoma de Mexico, Avenida Universidad S/N, Cuernavaca, Morelos 62210, Mexico
Meza-Peñaloza A.: Instituto de Investigaciones Antropologicas, Universidad Nacional Autonoma de Mexico, Distrito Federal Coyoacan, Mexico

The International Journal of Biostatistics, 2020, vol. 16, issue 2, 11

Abstract: For more than 50 years the Mean Measure of Divergence (MMD) has been one of the most prominent tools used in anthropology for the study of non-metric traits. However, one of the problems, in anthropology including palaeoanthropology (more often there), is the lack of big enough samples or the existence of samples without sufficiently measured traits. Since 1969, with the advent of bootstrapping techniques, this issue has been tackled successfully in many different ways. Here, we present a parametric bootstrap technique based on the fact that the transformed θ, obtained from the Anscombe transformation to stabilize the variance, nearly follows a normal distribution with standard deviation $\sigma = 1 / \sqrt{N + 1/2}$ σ = 1 / N + 1 / 2 , where N is the size of the measured trait. When the probabilistic distribution is known, parametric procedures offer more powerful results than non-parametric ones. We profit from knowing the probabilistic distribution of θ to develop a parametric bootstrapping method. We explain it carefully with mathematical support. We give examples, both with artificial data and with real ones. Our results show that this parametric bootstrap procedure is a powerful tool to study samples with scarcity of data.

Keywords: non-metric traits; Mean Measure of Divergence (MMD); parametric bootstrap; UPGMA clustering; populations separation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/ijb-2019-0117

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