The example of missing data (Appendix 9.E) can be fitted directly in the following way. Assume that the offsets are, for instance, constant across the first mode and that a principal component analysis model is sought including offsets across the first mode. Such a PCA model of the data held in the matrix X including offsets reads [Pg.254]

Initialize missing values with reasonable values. Then the data-set is complete and can be modeled by ordinary techniques. [Pg.254]

Fit the model including offsets to the (now complete) data set. For PCA, this amounts to centering the data and fitting the PCA model. [Pg.254]

Exchange missing values in the data matrix with the current model estimates. These estimates will improve the current estimates and thus provide a data set where the estimated missing elements are closer to the correct ones according to the (yet unknown) tme least squares model. [Pg.254]

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