Project Details
Description
Finite mixture models have been used for more than 100 years, but have seen a real boost in popularity over the last decades due to the tremendous increase in available computing power. These models find widespread application in many areas of applied statistics.
Three different areas of application can be distinguished: one major reason is to deal with unobserved heterogeneity, likely to be present in most data sets arising in marketing, economics, medicine or in the social sciences. A second application of mixture models is model-based clustering, classification and discrimination of socio-economic and related data. The third application has the purpose to estimate and approximate an unknown density function in a semi-parametric way.
The mixture modeling group at the IFAS is currently working on Bayesian and frequentist estimation of finite mixture models, model identification and inference as well as on different applications of mixture models.
| Status | Finished |
|---|---|
| Effective start/end date | 01.01.2012 → 31.12.2020 |
Fields of science
- 101024 Probability theory
- 504 Sociology
- 305 Other Human Medicine, Health Sciences
- 106 Biology
- 502 Economics
- 105 Geosciences
- 102009 Computer simulation
- 103 Physics, Astronomy
- 101 Mathematics
- 509 Other Social Sciences
- 101029 Mathematical statistics
- 101018 Statistics
- 504006 Demography
- 305907 Medical statistics
- 502051 Economic statistics
- 504004 Population statistics
- 105108 Geostatistics
- 509013 Social statistics
- 102035 Data science
- 101026 Time series analysis
- 106007 Biostatistics
- 102037 Visualisation
- 502025 Econometrics
- 504007 Empirical social research
- 101007 Financial mathematics
JKU Focus areas
- Sustainable Development: Responsible Technologies and Management
- Digital Transformation
-
Bayesian Latent Class Analysis with Shrinkage Priors: An Application to the Hungarian Heart Disease Data
Grün, B. & Malsiner Walli, G., 2018, ASMOD 2018 - Proceedings of the International Conference on Advances in Statistical Modelling of Ordinal Data. Stefania Capecchi, Francesca Di Iorio, Rosaria Simone (ed.). p. 13-24 12 p.Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings
-
Contributed Comment on "Bayesian Cluster Analysis: Point Estimation and Credible Balls" by Wade and Ghahramani
Frühwirth-Schnatter, S., Grün, B. & Malsiner Walli, G., 2018, In: Bayesian Analysis. 13, 2, p. 601-603 3 p.Research output: Contribution to journal › Article › peer-review
-
Model-Based Clustering
Grün, B., 2018, Handbook of Mixture Analysis. Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert (ed.). Chapman & Hall/CRC, p. 155-192 48 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
-
Modelling and prediction of COVID-19 outbreaks.
Stehlik, M. (Speaker)
06 Jan 2021Activity: Talk or presentation › Invited talk › science-to-science
-
REDACS: Regional emergency driven adaptive cluster sampling or effective COVID-19 prevalence
Stehlik, M. (Speaker)
16 Dec 2020Activity: Talk or presentation › Invited talk › science-to-science
-
Bayesian Model-Based Clustering with Flexible and Sparse Priors
Grün, B. (Speaker)
13 Sept 2019Activity: Talk or presentation › Invited talk › science-to-science