MM Algorithms for Smoothed Absolute Values
We discuss two different even and convex non-negative smooth approximations of the absolute value function and apply them to construct MM algorithms for least absolute deviation regression. Both uniform and sharp quadratic majorizations are constructed. As an example we use the Boston housing data. In our example sharp quadratic majorization is typically 10-20 times as fast as uniform quadratic majorization.