The Casualty Actuarial Society (CAS) has recently published an insightful new monograph entitled “Penalized Regression and Lasso Credibility.”
Authored by Thomas Holmes, Chief Actuary for the U.S. at Akur8, along with Mattia Casotto, Head of Product for the U.S. and Principal Scientist at Akur8, the monograph is a pivotal addition to the CAS’s peer-reviewed publication series. These publications are renowned for their comprehensive analysis on pivotal topics within the property and casualty actuarial sphere.
This new monograph is structured in three main sections, initiating with a detailed actuarial introduction to penalized regression. It highlights how this method transcends the capabilities of traditional Generalized Linear Models (GLMs) by enhancing stability and reliability in actuarial calculations. The publication connects penalized regression with credibility techniques, offering an intuitive and statistical understanding of this advanced approach.
Following the theoretical introduction, the monograph delves into the practical application of penalized regression. It introduces a robust actuarially sound method known as Lasso Credibility. This section is further enriched with a case study, demonstrating the practical application and benefits of Lasso Credibility, thereby providing readers with a clear grasp of this sophisticated technique.
Donna Royston, Managing Editor at CAS, expressed her gratitude towards the authors for their significant contributions. “The CAS Monograph Series is designed to expand the body of knowledge available to actuaries, offering in-depth explorations of key topics that have a direct impact on their work.
“We are grateful to Thomas Holmes and Mattia Casotto for their invaluable contributions to this important work. This latest release exemplifies our commitment to providing members with the research and resources they need to advance their careers.”
Thomas Holmes further elaborated on the goals of the monograph. ““The objective of this monograph is to help actuaries move beyond binary statistical significance and apply their expertise in credibility when evaluating model output. With practical guidance, case studies, and supporting code, it offers the actuarial community a comprehensive approach to applying lasso credibility in their ongoing project.”
Mattia Casotto commented on the technical advancements the monograph addresses. ““Traditional GLMs assume full dataset credibility, which can cause instability in parameter estimates for segments with sparse data. This monograph offers a solution by introducing penalized regression, specifically lasso penalization, to embed credibility within actuarial models.”
Access the monograph here.
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