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UID:20260601T140426EDT-0240HZsZRa@132.216.98.100
DTSTAMP:20260601T180426Z
DESCRIPTION:New Techniques for Modeling Non-life Insurance Claims\n\nTweedi
 e’s Compound Poisson model is a popular method to model data with probabil
 ity mass at zero and non-negative\, highly right-skewed distribution. Moti
 vated by wide applications of the Tweedie model in various fields such as 
 actuarial science\, we investigate a grouped elastic net method and a boos
 ted nonparametric method for the Tweedie model in the context of the gener
 alized linear model. For the grouped elastic net method\, in order to effi
 ciently compute the estimation coefficients\, we devise a two-layer algori
 thm that embeds the blockwise majorization descent method into an iterativ
 ely re-weighted least square strategy. In together with the strong rule\, 
 the proposed algorithm is implemented in an easy-to-use R package HDtweedi
 e\, and is shown to compute the whole solution path very efficiently. On t
 he other hand\, the linear form of the logarithmic mean in the Tweedie GLM
  sometimes can be too rigid for many applications. As a better alternative
 \, we propose a boosted nonparametric Tweedie model for pure premiums and 
 use a profile likelihood approach to estimate the index and dispersion par
 ameters. To our knowledge\, there is no existing nonparametric Tweedie met
 hod available before this work. Our method is capable of fitting a flexibl
 e nonlinear Tweedie model and capturing complex interactions among predict
 ors. We have also implemented this method in a user-friendly R package tha
 t includes a nice visualization tool for interpreting the fitted model.\n
DTSTART:20180329T190000Z
DTEND:20180329T200000Z
LOCATION:Room PK-5115 \, CA\, UQAM
SUMMARY:Yi Yang\, McGill University
URL:https://www.mcgill.ca/mathstat/channels/event/yi-yang-mcgill-university
 -286254
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