DC Field | Value | Language |
dc.contributor.author | Schaefer, Martin | |
dc.contributor.author | Radon, Yvonne | |
dc.contributor.author | Klein, Thomas | |
dc.contributor.author | Herrmann, Sabrina | |
dc.contributor.author | Schwender, Holger | |
dc.contributor.author | Verveer, Peter J. | |
dc.contributor.author | Ickstadt, Katja | |
dc.date.accessioned | 2021-12-23T16:20:51Z | - |
dc.date.available | 2021-12-23T16:20:51Z | - |
dc.date.issued | 2015 | |
dc.identifier.issn | 01679473 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/13626 | - |
dc.description.abstract | A new Bayesian approach for quantifying spatial clustering is proposed that employs a mixture of gamma distributions to model the squared distance of points to their second nearest neighbors. The method is designed to answer questions arising in biophysical research on nanoclusters of Ras proteins. It takes into account the presence of disturbing metacluster structures as well as non-clustering objects, both common among Ras clusters. Its focus lies on estimating the proportion of points lying in clusters, the mean cluster size and the mean cluster radius without depending on prior knowledge of the parameters. The performance of the model compared to other cluster methods is demonstrated in a comprehensive simulation study, employing a specific new class of spatial point processes, the double Matern cluster process. Further results and arguments as well as data and code are available as supplementary material. (C) 2015 Elsevier B.V. All rights reserved. | |
dc.description.sponsorship | Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG) [IC 5/3-1, VE 579/3-1, SCHW 1508/3-1]; German Ministry of Education and Research (BMBF)Federal Ministry of Education & Research (BMBF) [0315257]; Financial support of the Deutsche Forschungsgemeinschaft (DFG; Research Training Group 1032/2 `Statistical Modeling', grant no. IC 5/3-1 to K.I., grant no. VE 579/3-1 to P.J.V., grant no. SCHW 1508/3-1 to H.S.) and the German Ministry of Education and Research (BMBF; FORSYS initiative, grant no. 0315257 to P.J.V.) is gratefully acknowledged. We thank Alan E. Gelfand for helpful discussions on concepts and the presentation of the method as well as the editors and three anonymous reviewers for their constructive comments. | |
dc.language.iso | en | |
dc.publisher | ELSEVIER SCIENCE BV | |
dc.relation.ispartof | COMPUTATIONAL STATISTICS & DATA ANALYSIS | |
dc.subject | Cluster analysis | |
dc.subject | CLUTTER | |
dc.subject | Computer Science | |
dc.subject | Computer Science, Interdisciplinary Applications | |
dc.subject | FEATURES | |
dc.subject | Gamma mixture | |
dc.subject | Matern cluster process | |
dc.subject | Mathematics | |
dc.subject | Nearest neighbor distances | |
dc.subject | Point processes | |
dc.subject | RAS | |
dc.subject | Spatial statistics | |
dc.subject | Statistics & Probability | |
dc.title | A Bayesian mixture model to quantify parameters of spatial clustering | |
dc.type | journal article | |
dc.identifier.doi | 10.1016/j.csda.2015.07.004 | |
dc.identifier.isi | ISI:000361164900013 | |
dc.description.volume | 92 | |
dc.description.startpage | 163 | |
dc.description.endpage | 176 | |
dc.contributor.orcid | 0000-0001-6487-3634 | |
dc.identifier.eissn | 18727352 | |
dc.publisher.place | PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS | |
dcterms.isPartOf.abbreviation | Comput. Stat. Data Anal. | |