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Minimum Hellinger distance estimation in simple linear regression models; distribution and efficiency

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  • Pak, Ro Jin

Abstract

The minimum Hellinger distance estimation in simple linear regression models is considered. It is shown that the estimators of the slope parameter and the intercept parameter are asymptotically fully efficient, and that the estimator of the scale parameter is asymptotically reasonably efficient. Also, the asymptotic normality of these estimators is shown.

Suggested Citation

  • Pak, Ro Jin, 1996. "Minimum Hellinger distance estimation in simple linear regression models; distribution and efficiency," Statistics & Probability Letters, Elsevier, vol. 26(3), pages 263-269, February.
    • Handle: RePEc:eee:stapro:v:26:y:1996:i:3:p:263-269
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    References listed on IDEAS

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    1. Ayanendranath Basu & Bruce Lindsay, 1994. "Minimum disparity estimation for continuous models: Efficiency, distributions and robustness," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(4), pages 683-705, December.
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    Cited by:

    1. Ferdinand Österreicher & Igor Vajda, 2003. "A new class of metric divergences on probability spaces and its applicability in statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(3), pages 639-653, September.
    2. Ro Pak & Ayanendranath Basu, 1998. "Minimum Disparity Estimation in Linear Regression Models: Distribution and Efficiency," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(3), pages 503-521, September.

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