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Edgeworth expansions for errors-in-variables models

Author

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  • Babu, Gutti Jogesh
  • Bai, Z. D.

Abstract

Edgeworth expansions for sums of independent but not identically distributed multivariate random vectors are established. The results are applied to get valid Edgeworth expansions for estimates of regression parameters in linear errors-in-variable models. The expansions for studentized versions are also developed. Further, Edgeworth expansions for the corresponding bootstrapped statistics are obtained. Using these expansions, the bootstrap distribution is shown to approximate the sampling distribution of the studentized estimators, better than the classical normal approximation.

Suggested Citation

  • Babu, Gutti Jogesh & Bai, Z. D., 1992. "Edgeworth expansions for errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 42(2), pages 226-244, August.
    • Handle: RePEc:eee:jmvana:v:42:y:1992:i:2:p:226-244
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    Cited by:

    1. Gutti Babu & Yogendra Chaubey, 1996. "Asymptotics and bootstrap for inverse Gaussian regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(1), pages 75-88, March.

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