You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Generalized Linear Models Generalized Linear Models Course Topics Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, ...

We consider the problem of experimental design when the response is modeled by a generalized linear model (GLM) and the experimental plan can be determined sequentially. Most previous research on this ...

We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional ...

The general linear model is a further special case with Z = 0 and . The following two examples illustrate the most common formulations of the general linear mixed model.

Diet models based on goal programming (GP) are valuable tools in designing diets that comply with nutritional, palatability and cost constraints.

Solving a machine-learning mystery A new study shows how large language models like GPT-3 can learn a new task from just a few examples, without the need for any new training data Date: February 7 ...