[R] Computing a reliability index of a statistic with missing data

[Spencer Graves]

	  Have you considered some kind of binary time series model? 
'RSiteSearch("binary time series")' produced 150 hits.  One of the first 
20 mentioned "continuous-time hidden Markov chains" 
(http://finzi.psych.upenn.edu/R/library/repeated/html/chidden.html).  I 
don't know if this will help you or not, but it might be worth examining.

	  hope this helps.
	  Spencer Graves

Chaouch, Aziz wrote:
> Hi All,
> 
> I'd like to compute a kind of reliability index (RI) that would in a
> sense stand as a measure of reliability of a statistic (histogram etc)
> computed on a time serie with missing values. The final goal is that:
> 
> RI=1 for a perfect reliability
> RI=0 for a total unreliability (no data at all as an extreme case...)
> 
> The percentage of missing data is one indication: the more missing data,
> the less confidence we can have in the statistic. But the distribution
> of missing data throughout the data serie is important as well:
> independently of the number of missing data, if available data are
> regularily spaced in time the RI should be higher than if available data
> are irregulary spaced. As a measure of sampling regularity, I thought
> about computing the time to next record and then take its variance over
> the time interval on which the statistic is computed. The variance of
> the time to next record would be a measure of sampling regularity so
> that the final RI could be of the form:
> 
> RI=1 when n=0
> RI~1/n*var(T) 
> 
> with
> n=% of missing data
> T=time to next record (in hours)
> 
> However I need to "normalize" var(T) to use it to compute the RI. Does
> someone have an idea on how to do this (or another proposal to compute
> the RI)?
> 
> Thanks,
> 
> Aziz
> 
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