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The goal of expectile regression is to learn a model that predict the expectiles of a random variable $y$, perhaps given another random variable $x$. Suppose we want to learn the mean of $y$. One way to accomplish this is via the Mean-Squared Error (MSE) loss function. This is because the value of \(\hat{y}\) that minimizes MSE is the mean of \(y\):

Just as quantile regression generalized mean absolute error, expectile regression generalized mean squared error. For \(\tau \in (0, 1)\), the expectile regression loss function for the \(\tau\)th expectile is:

\[ER_{\tau}(\hat{y}) = |(\hat{y} - y)^2 (\tau - \mathbb{I}(\hat{y}-y < 0))|\]The loss function is convex and piece-wise quadratic.