 Effective Statistical Learning Methods for Actuaries II: Tree-Based Methods and ExtensionsMichel Denuit (),
Donatien Hainaut () and
Julien Trufin ()
No 2020035, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) Abstract: The present material is written for students enrolled in actuarial master programs and practicing actuaries, who would like to gain a better understanding of insurance data analytics. It is built in three volumes, starting from the celebrated Generalized Linear Models, or GLMs and continuing with tree-based methods and neural networks. This second volume summarizes the state of the art using regression trees and their various combinations such as random forests and boosting trees. This second volume also goes through tools enabling to assess the predictive accuracy of regression models. Throughout this book, we alternate between methodological aspects and numerical illustrations or case studies to demonstrate practical applications of the proposed techniques. The R statistical software has been found convenient to perform the analyses throughout this book. It is a free language and environment for statistical computing and graphics. In addition to our own R code, we have benefited from many R packages contributed by the members of the very active community of R-users. The open-source statistical software R is freely available from https://www.r-project.org/. The technical requirements to understand the material are kept at a reasonable level so that this text is meant for a broad readership. We refrain from proving all results but rather favor an intuitive approach with supportive numerical illustrations, providing the reader with relevant references where all justifications can be found, as well as more advanced material. These references are gathered in a dedicated section at the end of each chapter. The three authors are professors of actuarial mathematics at the universities of Brussels and Louvain-la-Neuve, Belgium. Together, they accumulate decades of teaching experience related to the topics treated in the three books, in Belgium and throughout Europe and Canada. They are also scientific directors at Detralytics, a consulting office based in Brussels. Within Detralytics as well as on behalf of actuarial associations, the authors have had the opportunity to teach the material contained in the three volumes of “Effective Statistical Learning Methods for Actuaries” to various audiences of practitioners. The feedback received from the participants to these short coursesgreatly helped to improve the exposition of the topic. Throughout their contacts with the industry, the authors also implemented these techniques in a variety of consulting and R&D projects. This makes the three volumes of “Effective Statistical Learning Methods for Actuaries” the ideal support for teaching students and CPD events for professionals. Pages: 288 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2020035 DOI: 10.1007/978-3-030-57556-4 Access Statistics for this paper More papers in LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) Voie du Roman Pays 20, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC. |
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