ML-Estimation in the Location-Scale-Shape Model of the Generalized Logistic Distribution
AbstractA 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.
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Bibliographic InfoPaper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 02-15.
Length: 16 pages
Date of creation: May 2002
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-08-26 (All new papers)
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.:
- Zelterman, D., 1987. "Parameter estimation in the generalized logistic distribution," Computational Statistics & Data Analysis, Elsevier, vol. 5(3), pages 177-184.
- Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-34, March.
- Filippo Domma & Pier Perri, 2009. "Some developments on the log-Dagum distribution," Statistical Methods and Applications, Springer, vol. 18(2), pages 205-220, July.
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