Identification and Estimation of Finite Mixture Models

01.10.2004 - 31.12.2007
Research funding project
Finite mixture models have been used for more than 100 years, but have seen a real boost in popularity over the last decade due to the tremendous increase in available computing power. Applications in disjoint scientific communities have led to the development of a lot of variants and extensions for special cases without proper analysis of many structural and statistical properties of the general model class. The EM algorithm provides a unifying framework for maximum likelihood estimation of parameters. However, the identification of these models was only considered for special cases and a thorough investigation of recent extensions and variants, as, e.g., mixtures of generalized linear models, is still missing. One major goal of this project is to develop a general theory for the identification of mixture models in a top-down approach. In addition to the theoretical investigations we will develop an open-source reference implementation within R, an environment for statistical computing and graphics. State of the art estimation techniques will be made available through a uniform and convenient user interface. Automatic model selection, diagnostic tools and checking of identifiability constraints for a specified model class and a given data set will be implemented, all of which are almost completely missing in existing software packages. The ultimate goal is a comprehensive methodological and computational toolbox for identification and estimation of finite mixture models.

People

Project leader

Project personnel

Institute

Grant funds

  • FWF - Österr. Wissenschaftsfonds (National) Austrian Science Fund (FWF)

Research focus

  • Beyond TUW-research focus: 100%

Keywords

GermanEnglish
MischmodelleMixture Models
IdentifizierbarkeitIdentifiability
RR
Unbeobachtete HeterogenitätUnobserved Heterogeneity

External partner

  • Marketing Research Innovation Centre University of Wollongong

Publications