ML-Estimation in the Location-Scale-Shape Model of the Generalized Logistic Distribution
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.
|Date of creation:||May 2002|
|Contact details of provider:|| Postal: Fach D 147, D-78457 Konstanz|
Web page: http://cofe.uni-konstanz.de
More information through EDIRC
|Order Information:|| Web: http://cofe.uni-konstanz.de Email: |
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-434, March.
- Zelterman, D., 1987. "Parameter estimation in the generalized logistic distribution," Computational Statistics & Data Analysis, Elsevier, vol. 5(3), pages 177-184.
When requesting a correction, please mention this item's handle: RePEc:knz:cofedp:0215. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ingmar Nolte)
If references are entirely missing, you can add them using this form.