Thanks Spencer, that is interesting but I must say I'm a bit lost with the terminology. I'll try to catch up but I'm not sure I need a complicated model (MC sounds complicated to me but it may not be...). I plan to use this reliability index just as an indication and I need to compute it in batch for several different charts so I try to keep the statistic as simple as possible but yet efficient.
Aziz -----Original Message----- From: Spencer Graves [mailto:[EMAIL PROTECTED] Sent: May 25, 2006 8:12 PM To: Chaouch, Aziz Cc: [email protected] Subject: Re: [R] Computing a reliability index of a statistic with missing data 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
