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The paper is devoted to the problem of recovery of one-dimensional functions given a set of noisy observations. Suppose that in addition, one is given a fixed set of a finite number of function estimates. Based on this set of estimates, it is necessary to construct a new estimator, the risk of which would be close to the risk of the "best" estimate (so-called oracle) in a given set. The "best" estimator is a minimizer of the risk over the given set of function estimators. New oracle inequalities for aggregation of regression function estimates in assumption of heteroscedasic Gaussian noise, namely, correlated Gaussian noise with different variances at each design point, have been proved.
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Ekaterina Krymova (2015) studied this question.
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