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

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  • Abberger, Klaus

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.

Suggested Citation

  • Abberger, Klaus, 2002. "ML-Estimation in the Location-Scale-Shape Model of the Generalized Logistic Distribution," CoFE Discussion Papers 02/15, University of Konstanz, Center of Finance and Econometrics (CoFE).
    • Handle: RePEc:zbw:cofedp:0215
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    File URL: https://www.econstor.eu/bitstream/10419/85186/1/dp02-15.pdf
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    References listed on IDEAS

    as
    1. Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-434, March.
    2. Zelterman, D., 1987. "Parameter estimation in the generalized logistic distribution," Computational Statistics & Data Analysis, Elsevier, vol. 5(3), pages 177-184.
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    Cited by:

    1. Filippo Domma & Pier Perri, 2009. "Some developments on the log-Dagum distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(2), pages 205-220, July.

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