Formulas for the exact LMS and LQS estimators

Abstract

The author presents the derivation of formulas for the calculation of critical values of the median function or the general version of it, namely, the quantile functions. In statistics, these functions are used to detect outliers in the data set and to make predictions that are resistant to outliers. Therefore, these formulas can also be used as estimators for these regressions. The fact that these formulas are able to calculate the global optimum gives the exact least median squares or the exact least quantile of squares estimators. The author provides the theoretical background for deriving these estimator formulas and derives the estimator formulas for regression models up to three parameters. In addition, the author provides guides for the derivation of formulas for other models, illustrates the use of these formulas, and emphasizes their properties that are useful for future works. One important conclusion is that each regression model has its own set of formulas.