From: Stavros Macrakis <macrakis_at_alum.mit.edu>

Date: Thu, 20 Nov 2008 10:43:27 -0500

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 20 Nov 2008 - 15:47:19 GMT

Date: Thu, 20 Nov 2008 10:43:27 -0500

I have some data measured with a coarsely-quantized clock. Let's say
the real data are

plot(q,type="l"); points(floor(q),col="red")

The simplest approach I can think of adds a uniform random variable of the size of the quantization:

plot(q,type="l"); points(floor(q),col="red"); points(floor(q)+runif(100,0,1),col="blue")

This gives pretty good results for uniform distributions, but less good for others (like exponential). Is there a better interpolation/smoothing function for cases like this, either Monte Carlo as above or deterministic?

Thanks,

-s

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 20 Nov 2008 - 15:47:19 GMT

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