Vol. 2016, 2016July 01, 2016 EDT
Using the Hayne MLE Models – A Practitioners Guide
Using the Hayne MLE Models – A Practitioners Guide
Mark R Shapland, Ping Xiao,
Maximum Likelihood EstimateReserve VariabilityReserve RangeDistribution of Possible OutcomesGeneralized Linear ModelBest Estimate
Articles in Vol. 2016, 2016
Vol. 2016, 2016
- Data & Technology Working Party ReportRaymond S Nichols
- Bornhuetter-Ferguson Initial Expected Loss Ratio Working Party PaperChandu C Patel
- Escaping Hindsight: Case Reserve Development Using the Reserve Runoff RatioJoseph A Boor
- On Equality and Inequality in Stationary PopulationsDavid A. SwansonLucky M. Tedrow
- Introduction to Bayesian Loss DevelopmentDavid R Clark
- Innovation Fueled by Risk ManagementAaron HalpertAaron Halpert
- An Extension to the Cape-Cod Method with Credibility Weighted SmoothingUri Korn
- Hierarchical Compartmental Models for Loss ReservingJake Morris
- The Actuary and Enterprise Risk Management: Integrating Reserve VariabilityMark R ShaplandJeffrey A. Courchene
- Using the Hayne MLE Models -- A Practitioners GuideMark R ShaplandPing Xiao
- Innovation in Crop Insurance: The Price-Flex® StoryMichael G Wacek
- A Practical Introduction to Machine Learning for ActuariesAlan ChalkConan McMurtrie
- Pitfalls of Predictive ModelingIra Robbin
- Insurance Risk-Based Capital with a Multi-Period Time HorizonRobert P Butsic
- Risk-Based Capital (RBC) Underwriting Risk Factor Safety LevelsAllan M KaufmanEmmanuel T BardisRobert P ButsicJose R CouretSholom FeldblumJennifer Wu
- Dependencies in Stochastic Loss Reserve ModelsGlenn G Meyers
Shapland, Mark R, and Ping Xiao. 2016. “Using the Hayne MLE Models – A Practitioners Guide.” CAS Forum 2016 (July).