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|
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- Kiefer, Nicholas M, 1978. "Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model," Econometrica, Econometric Society, vol. 46(2), pages 427-34, March.
- 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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