[R] statistics - hypothesis testing question

[S Ellison]

I may be miles off base, but could this be treated as a random-effects model, with the regression predictors as random effects grouped by week? And if so, could each set form a single lme() model, allowing you to compare the models via AIC's for 'quality' and anova for significance of the difference...? (After reading Pinheiro and Bates of course.. and not all mixed effects models can be compared directly, particularly using REML, if I read it correctly).



>>> "Greg Snow" <Greg.Snow at intermountainmail.org> 09/13/07 8:08 PM >>>

> I estimate two competing simple regression models, A and B 
> where the LHS is the same in both cases but the predictor is 
> different ( I handle the intercept issue based on other 
> postings I have seen ). I estimate the two models on a weekly 
> basis over 24 weeks. 
> So, I end up with 24 RSquaredAs and 24 RsquaredBs, so 
> essentally 2 time series of Rsquareds. This doesn't have to 
> be necessarily thought of as a time series problem but, is 
> there a usual way, given the Rsquared data, to test 
> 
> H0 : Rsquared B = Rsquared A versus H1 : Rsquared B > Rsquared A 
> 
> so that I can map the 24 R squared numbers into 1 statistic. 
> Maybe that's somehow equivalent to just running 2 big 
> regressions over the whole 24 weeks and then calculating a 
> statistic from those based on those regressions ?
> 
> I broke things up into 24 weeks because I was thinking that 
> the stability of the performance difference of the two models 
> could be examined over time. Essentially these are simple 
> time series regressions X_t = B*X_t-1 + epsilon so I always 
> need to consider whether any type of behavior is stable.  But 
> now I am thinking that,  if I just want one overall number,  
> then maybe I should be considering all the data simultaneously ? 
> 
> In a nutshell,  I am looking for any suggestions on the best 
> way to test whether Model B is better than Model A where
> 
> Model A :  X_t = Beta*X_t-1 + epsilon
> 
> Model B :  X_t = Betastar*Xstar_t-1 + epsilonstar
> 
> 
> Thanks fo your help.
> --------------------------------------------------------
> 
> This is not an offer (or solicitation of an offer) to 
> buy/se...{{dropped}}
> 
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide 
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
> 

______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

*******************************************************************
This email and any attachments are confidential. Any use, co...{{dropped}}