Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a ...

Given the importance of cereal grain seeds as the staple food and nutrition resources for humans and animals, and raw materials for food industry, understanding the genetic architecture underlying the ...

Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 45, No. 4 (1996), pp. 407-436 (30 pages) The authors review the applications of generalized linear models to actuarial ...

Mixed linear models are used to analyze data in many settings. These models have a multivariate normal formulation in most cases. The maximum likelihood estimator (MLE) or the residual MLE (REML) is ...

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 ...

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 ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with two-way interactions between ...

The standard linear regression model does not apply when the effect of one explanatory variable on the dependent variable depends on the value of another explanatory variable. In this case, the ...