Abstract
In this paper, the authors show that maximum likelihood estimates for a large class of commonly used regression models can be obtained by the method of iteratively weighted least squares, in which both the weights and the response arc adjusted from one iteration to the next. The proposed algorithm, sometimes known as “Fisher-scoring,” is an extension of Fisher’s (1935) method for computing maximum likelihood estimates in linear probit models. The same result was obtained independently by Bradley (1973) and Jennrich and Moore (1975), though not exploited to its full extent.
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© 1992 Springer-Verlag New York, Inc.
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McCullagh, P. (1992). Introduction to Nelder and Wedderburn (1972) Generalized Linear Models. In: Kotz, S., Johnson, N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4380-9_38
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DOI: https://doi.org/10.1007/978-1-4612-4380-9_38
Publisher Name: Springer, New York, NY
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