Abstract
The application of the ML method in linear regression requires a parametric form for the error density. When this is not available, the density may be parameterized by its cumulants (κ i ) and the ML then applied. Results are obtained when the standardized cumulants (γ i ) satisfy γ i =κ i+2/κ (i+2)/22 =O(v i) asv → 0 fori>0.
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Research financed in part by the Research Center of the Athens University of Economics and Business.
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Magdalinos, M.A. Approximate maximum likelihood estimation in linear regression. Ann Inst Stat Math 45, 89–104 (1993). https://doi.org/10.1007/BF00773670
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DOI: https://doi.org/10.1007/BF00773670