

FlashChat  Actuarial Discussion  Preliminary Exams  CAS/SOA Exams  Cyberchat  Around the World  Suggestions 


Thread Tools  Search this Thread  Display Modes 
#401




Quote:
The GLM used the Gamma family and log link to essentially do the same thing as log transforming but with the added restrictions of always producing a positive value. If you log transformed the variable you could you a regular gaussian GLM with the identity link but that model could produce negative values (not allowed by the target variable).
__________________
P, FM, MFE, MLC, C 
#403




Quote:
For an example Company A had 500 workers and 100 injuries Company B had 5 workers and 5 injuries If you were predicting the amount of injuries a company has per year, an offset could be applied so that the model takes into account the number of workers the company has when predicting the rate of injuries. In this example company A has 1 in 5 employees being injured, Company B has 1 in 1 employees being injured. The offset makes sure the model does not treat these as equal. The weight is about applying a singular predictor to a group of individuals with varying degrees of experience. For example a model predicts that a person has a 10% chance of going to the hospital within a given year. Person A was enrolled for 6 months Person B was enrolled for 12 months The weight given to these two individuals is .5 for Person A and 1 for person B. So looking at Person A there's only a 5% chance they went to the hospital, while Person B remains at 10% chance they went. Again, someone please correct me if I'm wrong.
__________________
P, FM, MFE, MLC, C 
#404




Quote:

#405




I'm not exactly certain, but looking at the code in the two examples you provided I'm not certain that either produces a different result. The GLM in the December exam defines an offset but I'm not versed enough in R coding to know if it has the same result as the GLM in module 6.
__________________
P, FM, MFE, MLC, C 
#406




When to binarize?
This might be a dumb question, but I'm wondering why they binarize for fitting the glm in the Hospital Readmissions but not June 2019's PA. Is it because we have too many factor variables in the June 2019 exam so it'd be way too much? Thanks to anyone who knows and can explain

#408




Quote:
Another reason could be the June exam was going to involve lasso/ridge regularization and that automatically does the binarization and would indicate any variables that might require that sort of split. This suggests the test creators thinking about later tasks while doing model work which isn't the best real world thinking.
__________________
P, FM, MFE, MLC, C 
Thread Tools  Search this Thread 
Display Modes  

