Big Data Featured in August Special Issue

摘要

The August 2016 issue of Technometrics is a special issue on Big Data analysis. A total of 11 papers are included, covering a wide range of topics in describing, analyzing, and computing Big Data. The first five papers propose numerical algorithms that can analyze Big Data fast. In "Orthogonalizing EM: A Design-Based Least Squares Algorithm" by Shifeng Xiong, Bin Dai, Jared Hiding, and Peter Z. G. Qian, an efficient iterative algorithm intended for various least squares problems, based on a design of experiments perspective, is proposed. The algorithm, called orthogonalizing EM (OEM), works for ordinary least squares and can be extended easily to penalized least squares. The main idea of the procedure is to orthogonalize a design matrix by adding new rows and then solve the original problem by embedding the augmented design in a missing data framework.