An admissibility condition of the least square estimator within the class of polynomial estimators

It is proved that, in the standard scheme (1.1) of linear regression, the admissibility of the least square estimator within the class of the polynomial equivariant estimators (4) is equivalent to the coincidence of $k+1$ first moments of the errors $\varepsilon_i$ with the corresponding moments of a normal distribution.