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Sebster
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Joined: July 18th, 2005, 2:24 pm

finding quantiles using Monte Carlo

January 26th, 2007, 8:50 am

Can anyone provide insights on computing quantiles of a distribution using Monte Carlo simulation? I'm mainly interested in what convergence bounds are available (for any approach) and in the existence of non-standard algorithms (such as the one discussed in the paper by Dagum, Karp, Luby and Ross).
 
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sanjaysivakumar
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finding quantiles using Monte Carlo

January 29th, 2007, 7:52 am

Convergence depends up on the sample that u generated. normally 10000 simulations........ Only trial and error approach and also depends upon ur interest of value(error).If you are taking about complex function like Bayesain function, then its also depends on the covering function which u choose to simulate.
 
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Sebster
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finding quantiles using Monte Carlo

February 2nd, 2007, 6:48 am

Yes, it certainly depends on the number of samples. I am looking for bounds such as Chebychev's inequality, which, for the mean, gives a bound on a the likelihood that the estimator error is greater than a certain threshold, explicitly computed based on the variance, the error threshold, and the number of simulations. Are there corresponding convergence bounds for quantile estimators? I imagine that there aren't, but I wonder what is typically used in this situation.
 
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Pannini
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finding quantiles using Monte Carlo

February 5th, 2007, 9:06 pm

Is "computing quantiles of a distribution using Monte Carlo simulation" different from bootstrapping quantiles?
Last edited by Pannini on February 4th, 2007, 11:00 pm, edited 1 time in total.
 
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mj
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finding quantiles using Monte Carlo

February 6th, 2007, 4:55 am

i think there's some discussion of this in Paul Glasserman's book
 
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Traden4Alpha
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finding quantiles using Monte Carlo

February 6th, 2007, 12:20 pm

I think you'll find that convergence is driven by two statistical factors:1. The probabilities of more or fewer samples falling above or below the quantile boundary which drives uncertainty about which sample or sample interval lies closest to the desired quantile boundary.2. The sampling density at the quantile boundary which is a function of N, the use of any kind of importance sampling, and the inverse of the PDF of the distribution. If you are trying to find a quantile boundary in a very thin part of the PDF (e.g. the tails or the mid-point of a deeply bimodal distribution) then you'll suffer from larger errors.
 
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Sebster
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finding quantiles using Monte Carlo

March 7th, 2007, 11:58 am

Perhaps it's the same. Can you recommend a good reference on the subject of bootstrapping quantiles? I haven't been able to pull up a good introduction searching online. QuoteOriginally posted by: PanniniIs "computing quantiles of a distribution using Monte Carlo simulation" different from bootstrapping quantiles?