Summary
This paper is concerned with the consistency of estimators in a single common factor analysis model when the dimension of the observed vector is not fixed. In the model several conditions on the sample sizen and the dimensionp are established for the least squares estimator (L.S.E.) to be consistent. Under some assumptions,p/n→0 is a necessary and sufficient condition that the L.S.E. converges in probability to the true value. A sufficient condition for almost sure convergence is also given.
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Kano, Y. Consistency conditions on the least squares estimator in single common factor analysis model. Ann Inst Stat Math 38, 57–68 (1986). https://doi.org/10.1007/BF02482500
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DOI: https://doi.org/10.1007/BF02482500