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|DOI (Digital Object Identifier) Link:||https://doi.org/10.1007/s00180-012-0314-4|
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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.
|Item Type:||Journal Article|
|Copyright Holders:||2012 Springer-Verlag|
|Keywords:||Broyden–Fletcher–Goldfarb–Shanno algorithm; iterative solution; Nelder–Mead algorithm|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Angela Noufaily|
|Date Deposited:||01 Mar 2012 15:03|
|Last Modified:||06 Oct 2016 04:53|
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