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 ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 66, No. 4 (2004), pp. 893-908 (16 pages) Generalized linear latent variable models (GLLVMs), as defined by ...

Incomplete data models typically involve strong untestable assumptions about the missing data distribution. As inference may critically depend on them, the importance of sensitivity analysis is well ...

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 ...