> > How can I estimate the probability distribution of a non-stationary > > signal?
> ... it depends what you mean by the whole question and by the various > parts of the question. > > ... in particular, what do you mean by "non-stationary" (for your > specific application) ? For example, if the siognal is non-stationary > because of seasonal effects, this leads to a set of possible answers > to your question that aren't available otherwise. It's no seasonal effect. In a first approximation, one might say that the signal consists of several time intervals where the signal is stationary, but with a different mean for each interval. > ... again, if you have available a set of multiple realisations of the > signal, this opens up another set of possibilities. I don't have multiple realisations. > ... what do you mean by "the probability distribution" ? Do you mean > the conditional distribution at some time-point or the marginal > distribution. If you mean the marginal distibution, this might be > something well-defined and might be estimable given some basic > assumptions, but it depends on what you mean by "non-stationary". What do you mean here by conditional distribution? Conditional on what? And what by the marginal distribution? Integrated over what? I have only one signal, isn't that just one random variable? Thanks, Geert . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
