Multivariate cumulative probit for age estimation using ordinal categorical data

Multivariate ordinal categorical data have figured prominently in the age estimation literature. Unfortunately, the osteological and dental age estimation literature is often disconnected from the statistical literature that provides the underpinnings for rationale analyses.

The aim of the study is to provide an analytical basis for age estimation using multiple ordinal categorical traits.

Data on ectocranial suture closure from 1152 individuals are analysed in a multivariate cumulative probit model fit using a Markov Chain Monte Carlo (MCMC) method.

Twenty-six parameters in a five variable analysis are estimated, including the 10 unique elements of the five × five correlation matrix. The correlation matrix differs substantially from the identity matrix one would assume under conditional independence among the sutures.

While the assumption of conditional independence between traits greatly simplifies the use of parametric models in age estimation, this assumption is not a necessary step. Further, in the analysis discussed here there are considerable residual correlations between ectocranial suture closure scores even after 'regressing out' the effect of age.