Abstract: We consider the binary classification problem of static and dynamic mixed data in this paper. Different from mixed categorical and numerical data, the dynamic variables in the new type of ...

Contributed by Emmanuel J. Candès, September 19, 2018 (sent for review June 21, 2018; reviewed by Nancy M. Reid and Huibin [Harry] Zhou) ...

1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...

Regression explains how changes in one factor influence another with clarity. Each regression type is suited for different data patterns and problems. Regression remains fast, reliable, and widely ...

The longitudinal microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures are correlated. We propose a ...

Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

High-throughput sequencing of 16S gene or metagenomes provides an unprecedented opportunity to discover microbes associated with traits such as clinical outcomes or environmental factors. However, the ...