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Using Generalized Regression in JMP® Pro to Create Robust Linear Models
Presenter: Brady Brady
Part 1: Overview and Case Study Using Generalized (Penalized) Regression
The presenter describes the benefits of Generalized Regression. He uses sample data about diabetes patients and their disease progression to show how to use JMP Pro lasso and elastic net shrinkage techniques to reduce prediction variance, handle non-normal and zero-inflated responses, model mean responses and select the best model interactively.
Part 2: Overview and Case Study Using Quantile Regression
The presenter uses sample birth weight data to show how to use JMP Pro Quantile Regression to handle situations where one does not want to model mean responses. He also discusses Cauchy regression and median regression.