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Semiparametric method for estimating paleodemographic profiles from age indicator data
Author(s) -
Müller HansGeorg,
Love Brad,
Hoppa Robert D.
Publication year - 2002
Publication title -
american journal of physical anthropology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.146
H-Index - 119
eISSN - 1096-8644
pISSN - 0002-9483
DOI - 10.1002/ajpa.10000
Subject(s) - osteology , mathematics , bayes' theorem , statistics , estimation , parametric statistics , time of death , function (biology) , sample size determination , econometrics , geography , medicine , bayesian probability , management , evolutionary biology , economics , biology , emergency medicine , archaeology
This paper addresses the problem of estimating an age‐at‐death distribution or paleodemographic profile from osteological data. It is demonstrated that the classical two‐stage procedure whereby one first constructs estimates of age‐at‐death of individual skeletons and then uses these age estimates to obtain a paleodemographic profile is not a correct approach. This is a consequence of Bayes' theorem. Instead, we demonstrate a valid approach that proceeds from the opposite starting point: given skeletal age‐at‐death, one first estimates the probability of assigning the skeleton into a specific osteological age‐indicator stage. We show that this leads to a statistically valid method for obtaining a paleodemographic profile, and moreover, that valid individual age estimation itself requires a demographic profile and therefore is done subsequent to its construction. Individual age estimation thus becomes the last rather than the first step in the estimation procedure. A central concept of our statistical approach is that of a weight function. A weight function is associated with each osteological age‐indicator stage or category, and provides the probability that a specific age indicator stage is observed, given age‐at‐death of the individual. We recommend that weight functions be estimated nonparametrically from a reference data set. In their entirety, the weight functions characterize the relevant stochastic properties of a chosen age indicator. For actual estimation of the paleodemographic profile, a parametric age distribution in the target sample is assumed. The maximum likelihood method is used to identify the unknown parameters of this distribution. As some components are estimated nonparametrically, one then has a semiparametric model. We show how to obtain valid estimates of individual age‐at‐death, confidence regions, and goodness‐of‐fit tests. The methods are illustrated with both real and simulated data. Am J Phys Anthropol 117:1–14, 2002. © 2002 Wiley‐Liss, Inc.