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This is an intermediate to advanced level course of regression methods. It covers simple and multiple linear regression models, including analysis of variance ...
The purpose of this homework is to provide a guided, hands-on tour through the properties of the least squares estimator, especially under common violations of ...
This white paper provides both practitioners and model reviewers (i.e. product managers and regulators) with recommendations for analysis and review of ...
The methods we study in this course generally assume and require complete data, without any missing values. The literature on miss-ing data problems is very large, and our goal is more to point out the issues than to provide solu-tions.
Our work presents an analysis of advanced regression models, including ridge, lasso, elastic net, decision tree, random forest, support vector regression,
The main difference from the conventional Simple Linear Regression is that each of the variables is complex. This makes the model more complicated than the one in real-valued domain.
