In the past have considered conditional independence in gaussian field (with log surprise measure) or non-parametric setting to try to understand correlation in 'tails'.
I think an algebraic approach (--> Pearl, Diaconis) here offers something
(does compressed sensing give us a signal processing type framework which could inform original question, though with an exotic perspective?)
Sensible?
Other alternatives in this direction?
Correlations of asynchronous & unequal-length arrays
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deeds
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JTDerp
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Correlations of asynchronous & unequal-length arrays
> In the past have considered conditional independence in gaussian field (with log surprise measure) or non-parametric setting to try to understand correlation in 'tails'.
By 'conditional independence' are you alluding to a binary type of filter for deciding when correlations are (semi) stable? A crude example: 'if volatility of A is less than x, and vol of B is less than y, then 1 else 0"?
On a side note, much thanks to you, ronin. Your perspective on this and many other threads is very useful/pragmatic.
By 'conditional independence' are you alluding to a binary type of filter for deciding when correlations are (semi) stable? A crude example: 'if volatility of A is less than x, and vol of B is less than y, then 1 else 0"?
On a side note, much thanks to you, ronin. Your perspective on this and many other threads is very useful/pragmatic.
"How dreadful...to be caught up in a game and have no idea of the rules." - C.S.