Abstract
Chapter 2 introduces the fundamental notion of the likelihood function and related quantities, such as the maximum likelihood estimate, the score function, and Fisher information. Computational algorithms are treated to compute the maximum likelihood estimate, such as optimization and the EM algorithm. The concept of sufficiency and the likelihood principle are finally discussed in some detail. Exercises are given at the end.
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Held, L., Sabanés Bové, D. (2014). Likelihood. In: Applied Statistical Inference. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37887-4_2
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DOI: https://doi.org/10.1007/978-3-642-37887-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37886-7
Online ISBN: 978-3-642-37887-4
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