On one problem of robust estimation from correlated observations

A robust estimate is obstained for a regression function parameter with random dependent noises. The error distribution density is assumed to belong to a distribution class with a constrained covariance matrix. The “worst” distribution which minimizes the Fisher information matrix is proved to be normal distribution. In the case of a linear regression model the resultant estimate is found to be optimal in the minimax sense.