On maximization of the likelihood for the generalized gamma distribution

Angela Noufaily () and M. Jones ()

Computational Statistics, 2013, vol. 28, issue 2, 505-517

Abstract: We explore computational aspects of likelihood maximization for the generalized gamma (GG) distribution. We formulate a version of the score equations such that the equations involved are individually uniquely solvable. We observe that the resulting algorithm is well-behaved and competitive with the application of standard optimisation procedures. We also show that a somewhat neglected alternative existing approach to solving the score equations is good too, at least in the basic, three-parameter case. Most importantly, we argue that, in practice far from being problematic as a number of authors have suggested, the GG distribution is actually particularly amenable to maximum likelihood estimation, by the standards of general three- or more-parameter distributions. We do not, however, make any theoretical advances on questions of convergence of algorithms or uniqueness of roots. Copyright Springer-Verlag 2013

Keywords: Broyden–Fletcher–Goldfarb–Shanno algorithm; Iterative solution; Nelder–Mead algorithm (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:2:p:505-517

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DOI: 10.1007/s00180-012-0314-4

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