L1-estimation in linear models with heterogeneous white noise
Necessary and sufficient conditions are given for the consistency of the L1-estimator of the regression parameter [beta] in linear models with independent but possibly nonidentically distributed errors. The heteroscedastic case is treated as a particular case. The asymptotic normality of is also established, under assumptions which are weaker than in related results on the asymptotics of the sample median in heteroscedastic location models.
Volume (Year): 45 (1999)
Issue (Month): 4 (December)
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References listed on IDEAS
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- Liese, F. & Vajda, I., 1994. "Consistency of M-Estimates in General Regression Models," Journal of Multivariate Analysis, Elsevier, vol. 50(1), pages 93-114, July.
- Marc Hallin & Ivan Mizera, 2001. "Sample heterogeneity and the asymptotics of M-estimators," ULB Institutional Repository 2013/2103, ULB -- Universite Libre de Bruxelles.
- Marc Hallin & Ivan Mizera, 1997. "Unimodality and the asymptotics of M-estimators," ULB Institutional Repository 2013/2217, ULB -- Universite Libre de Bruxelles.
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- Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(02), pages 186-199, June.
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