Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and ...

Data structures in modern applications frequently combine the necessity of flexible regression techniques handling, for example, non-linear and spatial effects with high dimensional covariate vectors.

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 ...

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 ...