Least absolute deviation (LAD) regression is a robust estimation technique in that it is less sensitive to the presence of outliers than OLS (but is less efficient than OLS when no outliers are present). It is equivalent to maximum likelihood estimation under a Laplace distribution model for ε.[16] Adaptive estimation. If we assume that error terms are independent of the regressors, ε i ⊥ x i {\displaystyle \varepsilon _{i}\perp \mathbf {x} _{i}} , then the optimal estimator is the 2-step MLE, where the first step is used to non-parametrically estimate the distribution of the error term.[17]