|WikiProject Statistics||(Rated Start-class, High-importance)|
|WikiProject Mathematics||(Rated Start-class, Mid-importance)|
This article redirects to multilevel model, but this is incorrect. A multilevel model may be mixed, but not necessarily so. A mixed model is one in which some of the effects are considered to be fixed and others are considered to be random. Someone with a bit more statistical knowledge than I have could perhaps re-write the mixed-model article. --Crusio (talk) 09:57, 2 September 2008 (UTC)
- Done, better late than never. I acknowledged the undoing of the redirect at WikiProject Statistics, so there will be higher visibility. Baccyak4H (Yak!) 02:55, 19 June 2009 (UTC)
There was a nebulous explanation of the details on how the mixed models (linear) are fit computationally. I gave a reference to the Lindstrom Bates article which describes iteratively fitting the linear model and estimating the variance components using the EM algorithm. I also cited R and SAS since this is how they do it. Might be good to format the procedures as computed code, instead of appearing as garbled jargon. My wiki-fu is not good enough to code them as such, yet.Saffloped (talk) 22:00, 12 September 2011 (UTC)
It seems that this sentence was added:
- More recently, methods for maximum likelihood estimation of mixed models have become more widely used than least-squares based methods.
This is not right nor is it wrong: it's confusing. Ordinary least squares minimizes the variance of the unweighted residuals. The point of the mixed model is that the variance components are not equal, so a more efficient estimation method is inverse variance weighting. The EM algorithm is necessary because it iteratively estimates the variance, applies the weights, and estimates model parameters. I say either remove the line, or don't speak of methods in terms of their "popularity" but their justification
Can we add dimensions to all the matrices, i.e.
* y is a vector of n observations * beta is a vector of fixed effects of length n * X is a matrix of ... with dimensions n x p where p is ... * u is a matrix of ... with dimensions n x q where q is ...