Christian Hennig, An empirical comparison and characterisation of nine popular clustering methods, «ADVANCES IN DATA ANALYSIS AND CLASSIFICATION», 2022, 16, pp. 201 - 229 [Scientific article]Open Access
Christian Hennig, Discussion of "Assumption-lean inference for generalised linear model parameters", «JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B STATISTICAL METHODOLOGY», 2022, 84, pp. 698 - 699 [Comment or similar]
Hennig, Christian, The controversy over p-values as an illustration of the difficulty of statistics: response to Mayo (2022), «CONSERVATION BIOLOGY», 2022, 36, pp. e13987 - e13987 [Comment or similar]
Ullmann T.; Hennig C.; Boulesteix A.-L., Validation of cluster analysis results on validation data: A systematic framework, «WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY», 2022, 12, Article number: e1444 , pp. 1 - 19 [Scientific article]Open Access
Hennig C., Christian Hennig’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer, «JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY», 2021, 184, pp. 446 - 447 [Comment or similar]
Batool F.; Hennig C., Clustering with the Average Silhouette Width, «COMPUTATIONAL STATISTICS & DATA ANALYSIS», 2021, 158, Article number: 107190 , pp. 1 - 18 [Scientific article]Open Access
Hennig, C, Contributed Discussion of Paganin, S., Herring, A. F. , Olshan, A. F. , Dunson, D. B., and The National Birth Defects Prevention Study: "Centered Partition Processes: Informative Priors for Clustering", «BAYESIAN ANALYSIS», 2021, 16, pp. 359 - 360 [Comment or similar]Open Access
Christian Hennig; Pietro Coretto, Non-parametric consistency for the Gaussian mixture maximum likelihood estimator, in: Cladag 2021 Book of Abstracts and Short Papers, Giovanni C. Porzio; Carla Rampichini; Chiara Bocci, 2021, pp. 116 - 119 (atti di: Cladag 2021, Firenze, September 9-11, 2021) [Contribution to conference proceedings]
Christian Hennig, Some results on identifiable parameters that cannot be identified from data, in: Book of Short Papers SIS 2021, Cira Perna; Nicola Salvati; Francesco Schirripa Spagnolo, 2021, pp. 1181 - 1186 (atti di: SIS 2021, Pisa, June 21-25, 2021) [Contribution to conference proceedings]
Akhanli S.E.; Hennig C., Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes, «STATISTICS AND COMPUTING», 2020, 30, pp. 1523 - 1544 [Scientific article]Open Access
Christian Hennig, Discussion on the meeting on ‘Signs and sizes:understanding and replicating statistical findings’, «JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY», 2020, 183, pp. 450 - 451 [Comment or similar]
Christian Hennig, Minkowski Distances and Standardisation for Clustering and Classification on High-Dimensional Data, in: Advanced Studies in Behaviormetrics and Data Science, Singapore, Imaizumi, Tadashi; Nakayama, Atsuho; Yokoyama, Satoru, 2020, pp. 103 - 118 (BEHAVIORMETRICS) [Chapter or essay]
Hausdorf B.; Hennig C., Species delimitation and geography, «MOLECULAR ECOLOGY RESOURCES», 2020, 20, pp. 950 - 960 [Scientific article]Open Access
Hennig, C, Review of: Ten Great Ideas about Chance, «PHILOSOPHIA MATHEMATICA», 2020, 28, pp. 282 - 285 [Review]
Espinosa, Javier; Hennig, Christian, A constrained regression model for an ordinal response with ordinal predictors, «STATISTICS AND COMPUTING», 2019, 29, pp. 869 - 890 [Scientific article]
