Minimum distance estimation in linear regression with unknown error distributions

Hira L. Koul

Statistics & Probability Letters, 1985, vol. 3, issue 1, 1-8

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.

Keywords: weighted; empiricals; qualitative; robustness; asymptotic; efficiency; heteroscedastic; gross; errors; influence; vectors (search for similar items in EconPapers)
Date: 1985
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Citations: View citations in EconPapers (2)

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