A.03.Robertson

Card Set Information

Author:
Exam8
ID:
162734
Filename:
A.03.Robertson
Updated:
2012-08-13 21:35:36
Tags:
NCCI hazard group excess ratio cluster
Folders:

Description:
NCCI's 2007 Hazard Group Mapping
Show Answers:

Home > Flashcards > Print Preview

The flashcards below were created by user Exam8 on FreezingBlue Flashcards. What would you like to do?


  1. Hazard group
    Collection of workers compensation classifications that have relatively similar expected XS loss factor over a broad range of limits
  2. 1993 NCCI review
    • used principal components analysis on 3 yrs
    • serious clm freq / total clm frq by class / statewide
    • serious clm indemnity severity by class / statewide
    • serious PP by class / statewide
  3. 2007 NCCI review
    • based on class ELF & cluster analysis
    • ELF vary by state, but not hazard group
    • Rj(L) = Σ wi,j si (L / μi,j)
    • wi,j = % loss due to injury type i
    • Si = state normlized xs ratio function
    • L / μi,j = entry ratio point
    • XS ratio vector = RC = (RC(L1), ..., RC(Ln))
  4. Corro & Engl
    A distribution is characterized by its excess ratios and so there is no loss of information in working with xs ratios rather than w size of loss
  5. Robertson hazard group credibility
    • z = min(n / n+ k * 1.5, 1), k = mean clm cnt
    • 1 - z given to RHG (previous hazard group)
    • k - using median: too low, z too high
    • exl medical only
    • incl only serious claims
    • k = mean of all classes w some minimum threshould → rejected, k was too high
  6. Building hazard groups - Limits
    • how to choose n and actual limits
    • correlation btwn neighboring XS ratios is high
    • looked at more limits, but they weren't gaining much info due to strong correlation for closer limits
  7. Euclidian distance btwn vectors (L2)
  8. Cluster method
    • if 2 objects are in diff clusters in the k cluster partition, then they will be in different clusters in all partitions w more than k elements
    • k-mean: for k clusters, group classes into k groups as to minimize the euclidian distance between elements
    • centroid: avg xs ratio vector for ith group
    •  |HGi| = # of classes in hazard group i
  9. Optimal # of clusters
    • Calinski & Harabasz statistic = [trace(B) / (k -1) / [trace(W)] / (n - k)]
    • n = # of classes, k = # of clusters
    • maximize it (high means higher btwn & lower within)
    • Cubic Clustering Criterion (CCC): compares amt of variance explained by a given set of clusters to rdm clusters
    • (-) less reliable when data is elongated (variables are highly correlated)
  10. Reasons why NCCI kept 7 hazard groups
    • Calinksi & Harabasz gave right answer more time than CCC on control data
    • CCC less reliable when var are highly correlated
    • both test indicated 7 when only class w high cred were used
    • 9 HG sln produces crossovers
  11. NCCI update - why B & E have many classes
    • XS ratios were credibility weighted w prior HG
    • low cred classes have similar vectors → end up together

What would you like to do?

Home > Flashcards > Print Preview