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 ...
DUBLIN--(BUSINESS WIRE)--Research and Markets(http://www.researchandmarkets.com/research/799091/deterministic_oper) has announced the addition of John Wiley and Sons ...
Generalized linear mixed models (GLMM) are useful in a variety of applications. With surrogate covariate data, existing methods of inference for GLMM are usually computationally intensive. We propose ...
Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse ...
Keywords: Statistical analyses. Regression models. Post-earthquake ignitions. Data analyses. California. Ground shaking. Generalized linear mixed models. Goodness-of ...
"Generalized Linear Models," Tuesday, April 6: The third LISA mini course for the 2010 Spring semester focuses on appropriate model building using generalized linear models. While multiple linear ...
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