Abstract
Statisticians1 often under-estimate the usefulness of general optimization methods in maximizing likelihoods and in other model-fitting problems. Not only are the general-purpose methods available in the S environments quick to use, they also often outperform the specialized methods that are available. A lot of the software we have illustrated in earlier chapters is based on the functions described in this. Code that seemed slow when the first edition was being prepared in 1993 now seems almost instant.
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© 2002 Springer Science+Business Media New York
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Venables, W.N., Ripley, B.D. (2002). Optimization. In: Modern Applied Statistics with S. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21706-2_16
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DOI: https://doi.org/10.1007/978-0-387-21706-2_16
Publisher Name: Springer, New York, NY
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