Estimation for the Multiple Factor Model when Data are Missing
PsychometrikaPublished 1 December 1979
Carl T. Finkbeiner
Citations152
SJR quartileQ1
SJR score1.90
SNIP2.06
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TL;DR
The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally.
Abstract
A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with 5 heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally.
Keywords
ChemistryMathematicsAgricultural and Biological Sciences
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