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Likelihood

In: Applied Statistical Inference

Author

Listed:
  • Leonhard Held

    (University of Zurich, Institute of Social and Preventive Medicine)

  • Daniel Sabanés Bové

    (University of Zurich, Institute of Social and Preventive Medicine)

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.

Suggested Citation

  • Leonhard Held & Daniel Sabanés Bové, 2014. "Likelihood," Springer Books, in: Applied Statistical Inference, edition 127, chapter 2, pages 13-50, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-37887-4_2
    DOI: 10.1007/978-3-642-37887-4_2
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