Hi R-users, I have 1020 time series ( each of length 10,000), say, X1,X2,......,X1020 and I want to perform Factor Analysis using 50 factors on their correlation matrix.
The issue is: for every series, I have a weight, i.e. *the series X_i has a pre-defined weight of w_i* ( i = 1,2,...., 1020). I want to estimate the factor loadings and specific variances in the model by optimizing the likelihood function (assuming multivariate normality, as usual). Is it possible to estimate the model parameters using the weights for each time series variable in the objective function? One comment here - For computational purposes or otherwise, it is *ok to change my objective function* (instead of taking the likelihood function, may be something like minimizing the weighted sum of squared specific variances for the variables would make sense). Any help with this will be really appreciated. Regards, Preetam -- Preetam Pal (+91)-9432212774 M-Stat 2nd Year, Room No. N-114 Statistics Division, C.V.Raman Hall Indian Statistical Institute, B.H.O.S. Kolkata. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.