Christian Martin Hennig | |
|---|---|
 Christian Hennig in 2023 | |
| Born | 1966 |
| Nationality | German |
| Education | University of Hamburg |
| Scientific career | |
| Fields | Statistics, particularly robust cluster analysis |
| Institutions | University of Bologna (Full Professor) |
| Thesis |
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| Doctoral advisor | Konrad Behnen |
| Website | https://www.unibo.it/sitoweb/christian.hennig/en/ |
Christian Martin Hennig (born 1966) [2] is a German statistician. His work focuses on robust cluster analysis.
Hennig completed his doctorate in 1997 at the University of Hamburg, Germany, under the supervision of Konrad Behnen. [3] From October 2001 until September 2003 he was University Lecturer (fixed term postdoctoral position) at the Seminar for Statistics, ETH Zürich. [4] Hennig was a lecturer at the Department of Statistical Science, University College London (UCL) from 2005 on, and became Senior Lecturer in 2010. [5] Since 2019 he is full Professor at the Dipartimento di Scienze Statistiche "Paolo Fortunati" at the University of Bologna. [6]
As of 2023 ,Hennig serves as the editor-in-chief of the journal Statistica . [7]
Hennig has published - partly with co-authors - about 130 papers in scientific journals and conference proceedings [8] as well as - together with M. Meila, F. Murtagh, and R. Rocci - a Handbook of Cluster Analysis:
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Peter J. Rousseeuw is a statistician known for his work on robust statistics and cluster analysis. He obtained his PhD in 1981 at the Vrije Universiteit Brussel, following research carried out at the ETH in Zurich, which led to a book on influence functions. Later he was professor at the Delft University of Technology, The Netherlands, at the University of Fribourg, Switzerland, and at the University of Antwerp, Belgium. Next he was a senior researcher at Renaissance Technologies. He then returned to Belgium as professor at KU Leuven, until becoming emeritus in 2022. His former PhD students include Annick Leroy, Hendrik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers.
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In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering bases this on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group.
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