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 ______________________________________________ [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
