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

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Author Info

  • Klaus Abberger

    ()
    (IFO Munich)

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.

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File URL: http://cofe.uni-konstanz.de/Papers/dp02_15.pdf
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Bibliographic Info

Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 02-15.

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Length: 16 pages
Date of creation: May 2002
Date of revision:
Handle: RePEc:knz:cofedp:0215

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Related research

Keywords: ECM algorithm; generalized logistic distribution; location-scale-shape model; maximum likelihood estimation;

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References

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  1. Zelterman, D., 1987. "Parameter estimation in the generalized logistic distribution," Computational Statistics & Data Analysis, Elsevier, vol. 5(3), pages 177-184.
  2. Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-34, March.
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Cited by:
  1. 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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