April 19, 2016

Random Partition Models and Complementary Clustering of Anglo–Saxon Placenames

Writing about web page http://projecteuclid.org/euclid.aoas/1453993094

Zanella, G. (2016). Random Partition Models and Complementary Clustering of Anglo-Saxon Placenames. Annals of Applied Statistics, 9(4), 1792–1822. See http://projecteuclid.org/euclid.aoas/1453993094, also http://arxiv.org/abs/1409.6994.

Common cluster models for multi-type point processes model the aggregation of points of the same type. In complete contrast, in the study of Anglo-Saxon settlements it is hypothesized that administrative clusters involving complementary names tend to appear. We investigate the evidence for such a hypothesis by developing a Bayesian Random Partition Model based on clusters formed by points of different types (complementary clustering).

As a result, we obtain an intractable posterior distribution on the space of matchings contained in a -partite hypergraph. We apply the Metropolis–Hastings (MH) algorithm to sample from this posterior. We consider the problem of choosing an efficient MH proposal distribution and we obtain consistent mixing improvements compared to the choices found in the literature. Simulated Tempering techniques can be used to overcome multimodality and a multiple proposal scheme is developed to allow for parallel programming. Finally, we discuss results arising from the careful use of convergence diagnostic techniques.

This allows us to study a data set including locations and place-names of 1316 Anglo-Saxon settlements dated approximately around 750–850 AD. Without strong prior knowledge, the model allows for explicit estimation of the number of clusters, the average intra-cluster dispersion and the level of interaction among place-names. The results support the hypothesis of organization of settlements into administrative clusters based on complementary names.

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  • Now you can read the hard–copy version of the above: Barnes, Clair, and Wilfrid Stephen Kendall. "Pe… by Wilfrid Kendall on this entry

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