Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models

Friedrich Leisch ()
Additional contact information
Friedrich Leisch: Ludwig-Maximilians-Universität München, Department of Statistics

A chapter in COMPSTAT 2008, 2008, pp 385-396 from Springer

Abstract: Abstract In this paper we show how only a few outliers can completely break down EM-estimation of mixtures of regression models. A simple, yet very effective way of dealing with this problem, is to use a component where all regression parameters are fixed to zero to model the background noise. This noise component can be easily defined for different types of generalized linear models, has a familiar interpretation as the empty regression model, and is not very sensitive with respect to its own parameters.

Keywords: mixture models; generalized linear models; robust statistics; R (search for similar items in EconPapers)
Date: 2008
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_32

Ordering information: This item can be ordered from
http://www.springer.com/9783790820843

DOI: 10.1007/978-3-7908-2084-3_32

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().