Abstract
The generalized gamma distribution offers a highly flexible family of models for lifetime data and includes a considerable number of distributions as special cases. This work deals with the use of the particle swarm optimization (PSO) algorithm in the maximum likelihood estimation of distributions of the generalized gamma family (GG-family) based on data with censored observations.We also discuss a procedure for testing whether a distribution that belong to GG-family is appropriate for lifetime data using the generalized likelihood ratio test principle. Finally, we present two illustrative applications using real data sets. For each data set, we use the PSO algorithm to fit several distributions of the GG-family simultaneously. Then, we test the appropriateness of each fitted model and select the most appropriate one using the Bayesian information criterion or the Akaike information criterion.
Keywords
- Particle Swarm Optimization
- Particle Swarm Optimization Algorithm
- Lifetime Data
- Reliability Function
- Inference Procedure
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Campos, M., Krohling, R.A., Borges, P. (2009). Particle Swarm Optimization for Inference Procedures in the Generalized Gamma Family Based on Censored Data. In: Mehnen, J., Köppen, M., Saad, A., Tiwari, A. (eds) Applications of Soft Computing. Advances in Intelligent and Soft Computing, vol 58. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89619-7_40
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DOI: https://doi.org/10.1007/978-3-540-89619-7_40
Publisher Name: Springer, Berlin, Heidelberg
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