Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental ...

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 ...

Flat prior (not usually recommended); Super-vague but proper prior: normal(0, 1e6) (not usually recommended); Weakly informative prior, very weak: normal(0, 10); Generic weakly informative prior: ...

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 ...

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 ...

Abstract: Everal real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example ...

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...