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Maximum Likelihood Estimation

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Effective Statistical Learning Methods for Actuaries I

Part of the book series: Springer Actuarial ((SPACLN))

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Abstract

This chapter recalls the basics of the estimation method consisting in maximizing the likelihood associated to the observations. The resulting estimators enjoy convenient theoretical properties, being optimal in a wide variety of situations. The maximum likelihood principle will be used throughout the next chapters to fit the supervised learning models.

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Notes

  1. 1.

    We comply here with standard statistical terminology, keeping in mind that the score has a very different meaning in actuarial applications, as it will become clear from the next chapters. To make the difference visible, we always speak of Fisher’s score to designate the statistical concept.

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Correspondence to Michel Denuit .

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Denuit, M., Hainaut, D., Trufin, J. (2019). Maximum Likelihood Estimation. In: Effective Statistical Learning Methods for Actuaries I. Springer Actuarial(). Springer, Cham. https://doi.org/10.1007/978-3-030-25820-7_3

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