Description: This abstract describes a paper that develops a method to estimate the two parameters of the discrete stable distribution.

Description: Recognizing an opportunity to help serve its members in this age of increased financial oversight, the Health Section of the Society of Actuaries commissioned a series of guides on the use of statistical techniques specifically geared for the work of health actuaries. The first guide in the series titled, 'Statistical Methods for Health Actuaries - IBNR Estimates: An Introduction,' is about the calculation of incurred but not reported [IBNR] health claims reserves. In particular, the guide focuses on the development of confidence intervals around IBNR estimates. This is an Excel workbook created to demonstrate the techniques discussed in the guide.

Description: This article discusses the use of parametric distributions to model health care cost.

Description: Discussion of machine learning applications currently being used to augment traditional actuarial modeling in the life insurance industry. This includes data science techniques for: Quantifying and explaining differences between model versions or reporting periods Building proxy models for actuarial model output using machine learning One-class classification for data integrity checks Applications of unsupervised learning (e.g., clustering and principal components analysis) to reduce model run time

Description: The minimum distance estimator proposed allows for the selection of the best fitting family member without prior distributional assumptions. It is shown that this estimator is consistent and can be made either robust or asymptotically efficient. Its asymptotic distribution is derived and and illustrated with automobile insurance data. From the Actuarial Research Clearing House 1990 Vol. 1

Description: This paper presents simple Bayesian graduation models that substantially ease the prior elicitation burden, and also describes a Monte Carlo integration approach that greatly reduces the computational load.

Description: In this paper, methods are presented that allow the systematic graduation of multidimensional data subject to linear constraints. With these constraints the practitioner can impose limitations on the graduation that agree with prior knowledge. Discussions of this paper are included.

Description: The article provides a high-level, non-technical, introduction to artificial neural networks (ANNs) and deep learning with an application to risk management. The method relies on typical ANN components such as nodes, activation functions, and optimization through gradient descent.

Description: The Cauchy integral formula is applied to derive various results on divided differences.