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Random partition models and complementary clustering of Anglo-Saxon place-names

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Zanella, Giacomo (2015) Random partition models and complementary clustering of Anglo-Saxon place-names. The Annals of Applied Statistics, 9 (4). pp. 1792-1822. doi:10.1214/15-AOAS884

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Official URL: http://dx.doi.org/10.1214/15-AOAS884

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Abstract

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 kk-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.

Item Type: Journal Article
Subjects: P Language and Literature > P Philology. Linguistics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Names, English (Old), Document clustering
Journal or Publication Title: The Annals of Applied Statistics
Publisher: Institute of Mathematical Statistics
ISSN: 1932-6157
Official Date: 2015
Dates:
DateEvent
2015Published
September 2014Submitted
Volume: 9
Number: 4
Number of Pages: 31
Page Range: pp. 1792-1822
DOI: 10.1214/15-AOAS884
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC)
Grant number: EP/D002060/1 (EPSRC)

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