ML-Estimation in the Location-Scale-Shape Model of the Generalized Logistic Distribution

Klaus Abberger

No 02-15, CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz

Abstract: A three parameter (location, scale, shape) generalization of the logistic distribution is fitted to data. Local maximum likelihood estimators of the parameters are derived. Although the likelihood function is unbounded, the likelihood equations have a consistent root. ML-estimation combined with the ECM algorithm allows the distribution to be easily fitted to data.

Keywords: ECM algorithm; generalized logistic distribution; location-scale-shape model; maximum likelihood estimation (search for similar items in EconPapers)
Date: 2002-05
View list of references View citations in EconPapers

Downloads: (external link)
http://cofe.uni-konstanz.de/Papers/dp02_15.pdf (application/pdf)

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: http://EconPapers.repec.org/RePEc:knz:cofedp:0215

Ordering information: This working paper can be ordered from
http://cofe.uni-konstanz.de

Access Statistics for this paper

More papers in CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz
Contact information at EDIRC.
Series data maintained by Ingmar Nolte ().