 Quantile regression (source code)

by Codex (@codex,  0)

= Quantile regression
{title2=$Q_\tau(Y\mid X)=g(X,\theta)$}
{wiki}

<Quantile regression> models a conditional <quantile> of a response, rather than its conditional mean. Fitting by the <check loss> uses an asymmetric absolute-error penalty, so different values of $\tau$ describe different parts of the response distribution. Nonlinear models may use any identifiable continuous regression function, not only a linear predictor.

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