A general approach for fitting a model to a data matrix by weighted le
ast squares (WLS) is studied. This approach consists of iteratively pe
rforming (steps of) existing algorithms for ordinary least squares (OL
S) fitting of the same model. The approach is based on minimizing a fu
nction that majorizes the WLS loss function. The generality of the app
roach implies that, for every model for which an OLS fitting algorithm
is available, the present approach yields a WLS fitting algorithm. In
the special case where the WLS weight matrix is binary, the approach
reduces to missing data imputation.