Likelihood

Leonhard Held and Daniel Sabanés Bové
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Leonhard Held: University of Zurich, Institute of Social and Preventive Medicine
Daniel Sabanés Bové: University of Zurich, Institute of Social and Preventive Medicine

Chapter 2 in Applied Statistical Inference, 2014, pp 13-50 from Springer

Abstract: 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.

Keywords: Likelihood Function; Fisher Information; Quadratic Approximation; Likelihood Principle; Raphson Algorithm (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-37887-4_2

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DOI: 10.1007/978-3-642-37887-4_2

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