Breakdown Point and Computation of Trimmed Likelihood Estimators in Generalized Linear Models

N. M. Neykov () and C. H. Müller ()
Additional contact information
N. M. Neykov: National Inst. of Meteorology and Hydrology, Bulgarian Academy of Sciences
C. H. Müller: Carl von Ossietzky University of Oldenburg, Dept. of Mathematics

A chapter in Developments in Robust Statistics, 2003, pp 277-286 from Springer

Abstract: Summary A review of the studies concerning the finite sample breakdown point (BP) of the trimmed likelihood (TL) and related estimators based on the d—fullness technique of Vandev (1993), and Vandev and Neykov (1998) is made. In particular, the BP of these estimators in the frame of the generalized linear models (GLMs) depends on the trimming proportion and the quantity N(X) introduced by Müller (1995). A faster iterative algorithm based on resampling techniques for derivation of the TLE is developed. Examples of real and artificial data in the context of grouped logistic and log-linear regression models are used to illustrate the properties of the TLE.

Keywords: Generalize Linear Model; Breakdown Point; Fatal Crash; Pearson Residual; High Breakdown Point (search for similar items in EconPapers)
Date: 2003
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:978-3-642-57338-5_24

Ordering information: This item can be ordered from
http://www.springer.com/9783642573385

DOI: 10.1007/978-3-642-57338-5_24

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 ().