Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Generally speaking, there are two types of outcomes (i.e. response) in statistical analysis: continuous and categorical responses. Linear Models (LM) are one of the most commonly used statistical ...

This article studies global testing of the slope function in functional linear regression models. A major challenge in functional global testing is to choose the dimension of projection when ...

Partially linear models provide a useful class of tools for modeling complex data by naturally incorporating a combination of linear and nonlinear effects within one framework. One key question in ...

Researchers have explained how large language models like GPT-3 are able to learn new tasks without updating their parameters, despite not being trained to perform those tasks. They found that these ...