What is hsh norm?

The HSH (Huber, Schmidt, and Strutz) norm is a robust estimator used in statistical data analysis for reducing the effects of outliers or errors. It is also known as the L1 norm or least absolute deviations. This norm is used in linear regression problems where the fitting of the model involves minimizing the sum of absolute residuals instead of the sum of squared residuals. It is less sensitive to outliers than the L2 norm (the sum of squared residuals) and hence, is widely used in applications where the presence of outliers is expected in the data. The HSH norm has found applications in various areas such as finance, environmental studies, and image processing.