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.
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.
References
Crawley MJ (2007) The R book. Wiley
Efron B, Hastie T (2016) Computer age statistical inference. Cambridge University Press
Klugman SA, Panjer HH, Willmot GE (2012) Loss models: from data to decisions, 4th edn. Wiley
Pawitan Y (2001) In all likelihood: statistical modelling and inference using likelihood. Oxford University Press
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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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DOI: https://doi.org/10.1007/978-3-030-25820-7_3
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