Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. A new class of recursive stochastic gradient algorithms designed for the k-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast...

Recursive estimates can be useful for diagnostic purposes, but algorithms for estimating dynamic models recursively with autocorrelated perturbations can be computationally complicated. Thus, we propose a Conditional Recursive Least Squares algorithm (CRLS): given initial full-sample consistent...