From: <cmdrnorton_at_poczta.onet.pl>

Date: Tue 07 Feb 2006 - 01:00:45 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Feb 07 01:10:05 2006

Date: Tue 07 Feb 2006 - 01:00:45 EST

Dear all,

In many papers regarding time series analysis
of acquired data, the authors analyze 'marginal
distribution' (i.e. marginal with respect to time)
of their data by for example checking

'cdf heavy tail' hypothesis.

For i.i.d data this is ok, but what if samples are correlated, nonstationary etc.?

Are there limit theorems which for example allow us to claim that for weak dependent, stationary and ergodic time series such a 'marginal distribution w.r. to time' converges to marginal distribution of random variable x_t , defined on basis of joint distribution for (x_1,…,x_T) ?

What if the correlation is strong (say stationary and ergodic FARIMA model) ?

Many thanks for your input

Norton

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Feb 07 01:10:05 2006

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