Abstract
This chapter presents the basic theoretical results of fitting log-linear models by maximum likelihood. The level of mathematical sophistication is consider-ably higher than in the rest of the book. The presentation assumes knowledge of advanced calculus, mathematical statistics, and large sample theory. Although the results in this chapter are proven in a different manner than for regular linear models, the results themselves are quite similar in nature. The common linear structure of the two techniques leads to the well known analogies between them. A familiarity with log-linear models at the level of, say Fienberg (1980), is assumed.
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© 1987 Springer Science+Business Media New York
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Christensen, R. (1987). Maximum Likelihood Theory for Log-Linear Models. In: Plane Answers to Complex Questions. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-1951-2_15
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DOI: https://doi.org/10.1007/978-1-4757-1951-2_15
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-1953-6
Online ISBN: 978-1-4757-1951-2
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