Journal of Data Science

Abstract: The use of contingency tables is widespread in archaeology. Cross tabulations are used in many different studies as a useful tool to syntheti cally report data, and are also useful when analyst wishes to seek for latent data structures. The latter case is when Correspondence Analysis (CA) comes into play. By graphically displaying the dependence between rows and columns, CA enables the analyst to explore the data in search of a meaningful inner structure. The article aims to show the utility of CA in archaeology in general and, in particular, for the identification of areas de voted to different activities within settlements. The application of CA to the data from a prehistoric village in north-eastern Sicily (P. Milazzese at Panarea, Aeolian Islands-Italy), taken as a case study, allows to show how CA succeeds in pinpointing different activity areas and in providing grounds to open new avenues of inquiry into other aspects of the archaeological doc umentation.

Abstract: Relative entropy identities yield basic decompositions of cat egorical data log-likelihoods. These naturally lead to the development of information models in contrast to the hierarchical log-linear models. A recent study by the authors clarified the principal difference in the data likelihood analysis between the two model types. The proposed scheme of log-likelihood decomposition introduces a prototype of linear information models, with which a basic scheme of model selection can be formulated accordingly. Empirical studies with high-way contingency tables are exem plified to illustrate the natural selections of information models in contrast to hierarchical log-linear models.