We consider the random design regression model with square loss. We propose a
method that aggregates empirical minimizers (ERM) over appropriately chosen
random subsets and reduces to ERM in the extreme case, and we establish sharp
oracle inequalities for its risk. We show that, under the $\varepsilon^{-p}$
growth of the empirical $\varepsilon$-entropy, the excess risk of the proposed
method attains the rate $n^{-2/(2+p)}$ for $p\in(0,2)$ and $n^{-1/p}$ for $p>2$
where $n$ is the sample size. Furthermore, for $p\in(0,2)$, the excess risk
rate matches the behavior of the minimax risk of function estimation in
regression problems under the well-specified model. This yields a conclusion
that the rates of statistical estimation in well-specified models (minimax
risk) and in misspecified models (minimax regret) are equivalent in the regime
$p\in(0,2)$. In other words, for $p\in(0,2)$ the problem of statistical
learning enjoys the same minimax rate as the problem of statistical estimation.
On the contrary, for $p>2$ we show that the rates of the minimax regret are, in
general, slower than for the minimax risk. Our oracle inequalities also imply
the $v\log(n/v)/n$ rates for Vapnik-Chervonenkis type classes of dimension $v$
without the usual convexity assumption on the class; we show that these rates
are optimal. Finally, for a slightly modified method, we derive a bound on the
excess risk of $s$-sparse convex aggregation improving that of Lounici [Math.
Methods Statist. 16 (2007) 246-259] and providing the optimal rate.

关于随机设计回归模型的统计学习研究，我们提出了一种聚合经验最小值的方法，并建立了其风险的尖锐 Oracle 不等式，进一步证明了在良好规定的模型下，统计估计和在错误规定的模型下的统计后悔的速率等价的结论。