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Regression with strongly correlated data

Author

Listed:
  • Jones, Christopher S.
  • Finn, John M.
  • Hengartner, Nicolas

Abstract

This paper discusses linear regression of strongly correlated data that arises, for example, in magnetohydrodynamic equilibrium reconstructions. We have proved that, generically, the covariance matrix of the estimated regression parameters for fixed sample size goes to zero as the correlations become unity. That is, in this limit the estimated parameters are known with perfect accuracy. Simple examples are shown to illustrate this effect and the nature of the exceptional cases in which the covariance of the estimate does not go to zero.

Suggested Citation

  • Jones, Christopher S. & Finn, John M. & Hengartner, Nicolas, 2008. "Regression with strongly correlated data," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 2136-2153, October.
    • Handle: RePEc:eee:jmvana:v:99:y:2008:i:9:p:2136-2153
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    References listed on IDEAS

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    1. Donald G. Morrison & David C. Schmittlein, 1991. "How Many Forecasters Do You Really Have? Mahalanobis Provides the Intuition for the Surprising Clemen and Winkler Result," Operations Research, INFORMS, vol. 39(3), pages 519-523, June.
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