Stability in Factor Analyses with Respect to Small Perturbations of the Data and to Departure from Gaussian Law of the Variables

  • Mireille Ludivine Bougeard
Part of the Ettore Majorana International Science Series book series (EMISS, volume 40)


In this paper, we consider the problem of approximations by sampling in the case of the principal component factor analysis. In a first part we recall the asymptotic results of Anderson, Arconte, Davis, Dauxois, Pousse and Romain about the distribution of the proper values of the operators. In a second step, we recall the principle of two non-parametric processes, namely jackknife and bootstrap. In a last stage, we compare the robustness of the asymptotic results mentioned earlier with the results obtained through non parametric processes by means of simulation and drawing of random samples in a given population.


Principal Component Analysis Covariance Matrix Asymptotic Result Observational Error Principal Component Factor Analysis 
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Copyright information

© Plenum Press, New York 1989

Authors and Affiliations

  • Mireille Ludivine Bougeard
    • 1
    • 2
  1. 1.IUTUniv. Paris XVille d’AvrayFrance
  2. 2.Observatoire ParisUA 1125 CNRSParisFrance

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