Economics at your fingertips  

Inference of finite mixture models and the effect of binning

Geissen Eva-Maria, Hasenauer Jan and Radde Nicole E. ()
Additional contact information
Geissen Eva-Maria: Institute for Systems Theory and Automatic Control, University of Stuttgart, Stuttgart, Germany
Hasenauer Jan: Hausdorff Center for Mathematics, HCM University of Bonn, Bonn, Germany
Radde Nicole E.: Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, D-70569Stuttgart, Germany

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 4, 20

Abstract: Finite mixture models are widely used in the life sciences for data analysis. Yet, the calibration of these models to data is still challenging as the optimization problems are often ill-posed. This holds for censored and uncensored data, and is caused by symmetries and other types of non-identifiabilities. Here, we discuss the problem of parameter estimation and model selection for finite mixture models from a theoretical perspective. We provide a review of the existing literature and illustrate the ill-posedness of the calibration problem for mixtures of uniform distributions and mixtures of normal distributions. Furthermore, we assess the effect of interval censoring on this estimation problem. Interestingly, we find that a proper treatment of censoring can facilitate the estimation of the number of mixture components compared to inference from uncensored data, which is an at first glance surprising result. The aim of the manuscript is to raise awareness of challenges in the calibration of finite mixture models and to provide an overview about available techniques.

Keywords: finite mixture model; interval censoring; model selection; unbounded likelihood (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1515/sagmb-2018-0035

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

Page updated 2021-05-24
Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:4:p:20:n:2