Thanks Spencer. I'm saving all of this useful info. I've never used lme or nlme and know next to nothing about mixed models so I'll definitely have a learning curve. It sounds interesting though<br />and I'll let you know how things work out.<br /><br /><br /><br /><br /><p>On May 23, 2009, <strong>spencerg</strong> <spencer.graves@prodsyse.com> wrote: </p><div class="replyBody"><blockquote style="border-left: 2px solid #267fdb; margin: 0pt 0pt 0pt 1.8ex; padding-left: 1ex"> The "lme" function maximizes a likelihood specified by the model <br />implicit in the formula. <br /><br /><br /> If you are worried about a lack of normality, I suggest that <br />before you do a lot of work to invent your own thing, you try "lme", and <br />plot residuals, estimated coefficients using "coef.lme", etc. If you <br />see evidence that the normal likelihood is not adequate, you would then <br />have justification for doing something more complicated. <br /><br /><br /> Spencer Graves <br /><br /><a href="mailto:markleeds@verizon.net" target="_blank" class="parsedEmail">markleeds@verizon.net</a> wrote:<br />> Hi Ajay: That's a good point. It's really a maximization of the sum of the <br />> likelihoods of the individual series if you assume independent shocks. I'd <br />> have to look inside arima ( when I got the courage ),<br />> extract the likelihood piece and then put ithe sum inside say optim That's why I <br />> was kind of hoping there might be something out there , even if independence <br />> needed to be assumed.<br />><br />> But, I don't think your idea is quite equivalent to the DLM approach because <br />> there you are able to<br />> specify correlation structure on the multiple series rather than assuming <br />> independence of each series. For my problem, I have no idea whether relaxing the <br />> assumption as your idea would do, would matter ? All these things are <br />> approximations to reality anyway so who ever knows ?<br />><br />> I'll I either go the DLM route ( spencer mentioned that I should also look at <br />> Pinheiro and Bates ) or your route but I'm not there yet anyway. I was just <br />> thinking about this for the down the road if and<br />> when I need it and I hope that I do because that would indicate progress.<br />><br />><br />><br />><br />><br />><br />><br />> On May 23, 2009, *Ajay Shah* <<a href="mailto:ajayshah@mayin.org" target="_blank" class="parsedEmail">ajayshah@mayin.org</a>> wrote:<br />><br />> On Fri, May 22, 2009 at 08:13:25PM -0500, <a href="mailto:markleeds@verizon.net" target="_blank" class="parsedEmail">markleeds@verizon.net</a><br />> <mailto:<a href="mailto:markleeds@verizon.net" target="_blank" class="parsedEmail">markleeds@verizon.net</a>> wrote:<br />> > Hi everyone: Normally, if one has a single realization of a time series<br />> and one wants to estimate<br />> > say an ARMA(p,q) , where p and q are known ( for simplicity ) then one<br />> estimates it and that's that.<br />> ><br />> > But, suppose that one has more than one realization of the time series (<br />> assuming each series is the same length) and yet still wants to estimate the<br />> "best" arma(p,q) , over all the realizations, again where p and q are known.<br />><br />> Could we perhaps think of this as follows.<br />><br />> We are holding two realisations from the same process:<br />> x1, x2, ... xN<br />> y1, y2, ... yN<br />><br />> and let's suppose these two realisations are completely<br />> independent. Think of two parallel experiments running with the<br />> identical data generating process but a different set of random<br />> shocks.<br />><br />> Then you could construct the overall log likelihood of what you have<br />> observed as logl(theta; x) + logl(theta; y) and maximise that.<br />><br />> Is there an existing R function off the shelf which yields the ARMA<br />> log likelihood? If so then it should be easy to put together an<br />> overall logl() function for this problem which can be then given to<br />> optim() to do estimation.<br />><br />> -- <br />> Ajay Shah <a href="http://www.mayin.org/ajayshah" target="_blank" class="parsedLink">http://www.mayin.org/ajayshah</a><br />> <a href="mailto:ajayshah@mayin.org" target="_blank" class="parsedEmail">ajayshah@mayin.org</a> <mailto:<a href="mailto:ajayshah@mayin.org" target="_blank" class="parsedEmail">ajayshah@mayin.org</a>> <a href="http://ajayshahblog.blogspot.com" target="_blank" class="parsedLink">http://ajayshahblog.blogspot.com</a><br />> <*(:-? - wizard who doesn't know the answer.<br />><br />> <br />> ------------------------------------------------------------------------<br />><br />> _______________________________________________<br />> <a href="mailto:R-SIG-Finance@stat.math.ethz.ch" target="_blank" class="parsedEmail">R-SIG-Finance@stat.math.ethz.ch</a> mailing list<br />> <a href="https://stat.ethz.ch/mailman/listinfo/r-sig-finance" target="_blank" class="parsedLink">https://stat.ethz.ch/mailman/listinfo/r-sig-finance</a><br />> -- Subscriber-posting only.<br />> -- If you want to post, subscribe first.<br /><br /></blockquote></div>