It can be challenging to perform an integrative statistical analysis of multi-view high-dimensional data acquired from different experiments on each subject who participated in a joint study. Canonical Correlation Analysis (CCA) is a statistical procedure for identifying relationships between such data sets. In that context, Structured Sparse CCA (ScSCCA) is a rapidly emerging methodological area that aims for robust modeling of the interrelations between the different data modalities by assuming the corresponding CCA directional vectors to be sparse. Although it is a rapidly growing area of statistical methodology development, there is a need for developing related methodologies in the Bayesian paradigm. In this manuscript, we propose a novel ScSCCA approach where we employ a Bayesian infinite factor model and aim to achieve robust estimation by encouraging sparsity in two different levels of the modeling framework. Firstly, we utilize a multiplicative Half-Cauchy process prior to encourage sparsity at the level of the latent variable loading matrices. Additionally, we promote further sparsity in the covariance matrix by using graphical horseshoe prior or diagonal structure. We conduct multiple simulations to compare the performance of the proposed method with that of other frequently used CCA procedures, and we apply the developed procedures to analyze multi-omics data arising from a breast cancer study.

As if active portfolio managers didn't have enough challenges from computer-driven passive investing strategies, now machines are directly horning in on their territory. San Francisco-based EquBot LLC is launching the first ever exchange-traded fund to use artificial intelligence, according to a company statement on Tuesday. Employing International Business Machines Corp.'s Watson platform, the AI Powered Equity ETF, ticker AIEQ, will attempt to mimic an army of equity research analysts working around the clock, according to Art Amador, co-founder of EquBot. "There has been an explosion of information," Amador said by phone. "AI provides a more informed way of investing."

Uber and Lyft may have changed lives in the Big American City, but they're hardly ubiquitous. Just 15 percent of Americans use these services, according to the Pew Research Center. One-third have never heard of them. The ridesharing giants do have an excellent way to build a bigger, less urban customer base: teaming up with government. In Florida, in New Jersey, and in Colorado, Uber and Lyft have partnered with municipalities to solve first-mile, last-mile problems, ferrying riders to bus stops, train stations, or even their homes for subsidized fares.