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#1
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Question says there is an uneven distribution of business along other class dimensions not being analyzed.
In this case, after we derive the LR, shouldn't we rebase to (t1,a2) before multiplying by the current relativities? Otherwise, how do we adjust for dimensions not considered? |
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#2
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I think the current raletivities given are the current average relativities across other class dimensions. So as soon as LRs have been multiplied by them they are now adjusted for class distribution differences.
However, I agree that I would rebase to the base class given, but that I don't think has got anything to do with uneven class distribution. |
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#4
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I think re-basing ends up being equivalent to multiplication by a scalar, across the entire minimum bias equation. As such, you can do this at the beginning or the end of the calculation, without altering the outcome.
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#5
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Only if convergence occurs at one iteration. Otherwise, the first iteration (or non-converged relativities) are not the same before and after rebasing
I would always rebase before iteration if I know what I need to rebase to...and then keep rebasing at each step since ietration doesn't guaranty base will be 1 at each step |
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#7
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I would think so
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#8
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http://www.actuarialoutpost.com/actu...d.php?t=174286
Is this what you get when rebasing? What would the assumption be for not rebasing? |
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#9
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If you just do Loss Ratio times Curent relativities then what you end up with is loss ratios. These are not relativities. If the question says what are the realitivities, you would need to "pull out" the base class loss ratio to make your answer a relativity.
If you do Loss Ratio*CR/Base class LR then you have relativities right upfront adjusted for distributional biases. If you proceed with this you will end up with loss ratio relativities. Does that help? |
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#10
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Quote:
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