This is a preview. Log in through your library . Abstract This study focuses on using a high-dimensional error-in-variables regression to identify a small number of important interpretable factors ...

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

When you’re building a machine learning model you’re faced with the bias-variance tradeoff, where you have to find the balance between having a model that: Is very expressive and captures the real ...

We consider the generic regularized optimization problem $\hat{\beta}(\lambda)={\rm arg}\ {\rm min}_{\beta}\ L({\rm y},X\beta)+\lambda J(\beta)$. Efron, Hastie ...

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...