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I am writing my thesis and use a GBM to model insurance claim frequency. As I am currently writing the background, I am looking for good references on GBM. Do you have any recommendations for me? Any essential papers, that I should read/use as reference.

Thanks in advance!

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I can think of the following:

Friedman, J. (2001). Greedy boosting approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232. doi: 10.1214/aos/1013203451 link

Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Ann. Stat. 28, 337–407. doi: 10.1214/aos/1016218222 link

J. H. Friedman. Stochastic gradient boosting. Computational Statistics and Data Analysis, 38(4):367–378, 2002. link

Friedman, Hastie, and Tibshirani (2000) paper discusses the first successful boosting algorithm, Adaboost from a statistical point of view. Friedman (2001) and the companion paper Friedman (2002) extended the work to generalize Adaboost to Gradient Boosting in order to handle a variety of loss functions.

There is a paper I came across but haven't had the chance to go in depth:

(May 4) David Mease and Abraham Wyner (2008). Evidence contrary to the statistical view of boosting. Journal of Machine Learning Research, vol 9, pp 131--156 link

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  • $\begingroup$ Thanks a lot, I will definetly look into these! $\endgroup$
    – Unknown
    Commented May 3, 2020 at 7:44

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