Some Perspectives about Generalized Linear Modeling
Alan Agresti, Distinguished Professor Emeritus University of Florida
Lunedì 23 maggio ore 12 Aula Salzano
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Abstract: This talk discusses several topics pertaining to generalized linear
modeling. With focus on categorical data, the topics include (1) bias
in using ordinary linear models with ordinal categorical response
data, (2) interpreting effects with nonlinear link functions, (3)
cautions in using Wald inference (tests and confidence intervals) when
effects are large or near the boundary of the parameter space, and (4)
the behavior and choice of residuals for GLMs. I will present few new
research results, but these topics got my attention while I was
writing the book `Foundations of Linear and Generalized Linear
Models,' recently published by Wiley.