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Minimum distance estimation in linear regression with unknown error distributions

Author

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  • Koul, Hira L.

Abstract

This paper discusses minimum distance (m.d.) estimators of the paramter vector in the multiple linear regression model when the distributions of errors are unknown. These estimators are defined in terms of L2-distances involving certain weighted empirical processes. Their finite sample properties and asymptotic behavior under heteroscedastic, symmetric and asymmetric errors are discussed. Some robustness properties of these estimators are also studied.

Suggested Citation

  • Koul, Hira L., 1985. "Minimum distance estimation in linear regression with unknown error distributions," Statistics & Probability Letters, Elsevier, vol. 3(1), pages 1-8, February.
    • Handle: RePEc:eee:stapro:v:3:y:1985:i:1:p:1-8
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    Cited by:

    1. Li, Linyuan, 2003. "On Koul's minimum distance estimators in the regression models with long memory moving averages," Stochastic Processes and their Applications, Elsevier, vol. 105(2), pages 257-269, June.
    2. Hira L. Koul & Indeewara Perera & Narayana Balakrishna, 2023. "A class of Minimum Distance Estimators in Markovian Multiplicative Error Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 87-115, May.

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