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12:00 AM
In an experiment where subjects are divided in several groups, we have a random factor A (subjects) nested within fixed factor B (groups). So random is nested within fixes. This I understand. But in the linked example a fixed factor is supposed to be nested within a random factor. How should I think about it? Something fixed nested within subjects? Is it even possible?
I expect this to be a real dumb question that somebody can quickly answer here...
 
@wolframalpha "formal" is almost an antonym of "rule of thumb"
@ssdecontrol a typical way to handle irregularly spaced time series is with continuous-time methods. (If it's a regularly spaced series with a lot of missing values you can easily deal with them using state-space methods; consequently an irregularly-spaced series with all inter-observation gaps a multiple of some fixed interval don't necessarily require anything more than standard ways of dealing with missingness in time series)
In part it depends on what models you want to estimate.
As long as you have the data to estimate the model you want to fit, a likelihood's a likelihood and a posterior is a posterior; there's EM algorithms and MCMC and so on that can sometimes be adapted.
I haven't heard of Eckner ... what sort of models does he use?
...
e.g. see Peter Brockwell "On the Use of Continuous-time ARMA Models in Time Series Analysis" Chapter in
*Athens Conference on Applied Probability and Time Series Analysis*,
pp 88-101
... http://link.springer.com/chapter/10.1007%2F978-1-4612-2412-9_7
The reference list at that link gives lots of relevant books and papers.
 
1:02 AM
@amoeba The way I understand it, "B is nested within A" is misleading, if not incorrect. B is just a fixed within-subject factor (if A is subject).
 
1:20 AM
@mark999 Thanks! But is the aov formula correct?
@mark999 I spent several hours trying to understand this issue, so I guess I should post a proper question, even if it's dumb. Here it goes: stats.stackexchange.com/questions/232109.
 
1:36 AM
@amoeba Yes the formula is correct. I don't have access to it at the moment, but a while ago I verified that using that aov formula gives results that are the same as those given in an example in "Applied Linear Statistical Models" by Kutner et al., and the same as what you get with an equivalent model with lme if the data is balanced.
 

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